Training Coach

Doctrine

A report may cite any of these as doctrine-N.

1. doctrine-1Contract freeze and fingerprint discipline

   The policy I/O contract (observation layout, scales, history semantics, action pipeline) is frozen and fingerprinted; every exported policy is stamped and verified; contract changes ship as new versioned profiles that leave old artifacts bit-identical, and old policies run forever under their era's pinned profile.

   Case. The 215-dim omni contract was frozen with a three-machine digest; the one contract-level extension (lateral feed-forward) went in as a new `omni_ff` profile with the old profile provably untouched, and the contract checker caught two real wiring bugs before any training (`contract-freeze-and-checker`). A silently changed gait-clock default would have fed old policies a 25% slower clock - closed by pinned legacy profiles (`legacy-profile-pinning`). A stale derived USD forked plant mass 2.2% until an automated source-vs-derived instrument gated it (`derived-asset-staleness-check`). A gain profile is part of the closed loop a policy was trained in and belongs in its stamp; the recovery line's anchored authority was left out of its manifest and recorded as the gap not to repeat (`gain-profile-belongs-in-the-stamp`), and a second policy behind a deploy-side switch made the handoff state itself a contract (`recovery-two-policies-and-a-state-machine`, `walk-recovery-fsm-handoff`).

   Coach application. On any proposal touching obs/action semantics, defaults, or derived assets: demand the version/profile plan, the fingerprint update, and the checker extension in the same change; flag any old artifact that would run under new defaults.

   contract-freeze-and-checkerlegacy-profile-pinningderived-asset-staleness-checkgain-profile-belongs-in-the-stamprecovery-two-policies-and-a-state-machinewalk-recovery-fsm-handoff

2. doctrine-2Attribution by resolved training params - never eval-override knobs

   Capability differences between lineages are explained only by digging each lineage's *resolved* training configuration and eliminating columns; evaluation-side override knobs (kd-scale, power-scale, cycle-time) act on the plant for *every* policy and may serve as deployment mitigations but never as explanations.

   Case. Low-friction robustness across 8 lineages x 3840 cells was traced to kd DR *bandwidth* - every lineage had ground friction pinned to (1.0,1.0), so "trained friction" could not be the axis; the parameter axis and the plant axis were explicitly separated after the first attribution conflated them (`kd-bandwidth-mu-law-attribution`). "Weak turning" on hardware was a power-scale plant effect, not a training gap (`deploy-knob-attribution-before-retraining`); slowing the deploy clock was out-of-distribution, not a feature (`cycle-time-override-is-ood`). The ground truth for what a run trained under is the logged per-run config, not the source tree (`resolved-config-is-source-of-truth`).

   Coach application. Whenever asked "why is lineage A better", require the resolved-param table first; kill zero-variance columns; refuse explanations phrased in eval-knob terms; when a knob helps, label it deployment mitigation.

   kd-bandwidth-mu-law-attributiondeploy-knob-attribution-before-retrainingcycle-time-override-is-oodresolved-config-is-source-of-truth

3. doctrine-3PASS gates become constraints; FAIL gates become objectives

   Once a skill passes its gate, that gate converts into a standing regression constraint (budget <= 2/20 against the parent baseline) for all later training; gates currently failing are the only legitimate objectives of the next rung.

   Case. The C ladder ran one frozen 13-cell x 20-seed matrix at every rung with promotion = "new skill PASS and old skills within regression budget"; C1 was stopped and re-rooted precisely because it trained away the root's backward PASS (`fixed-acceptance-matrix-per-rung`, `preregistered-stop-criteria-per-rung`). The C4 product shipped only at 260/260 cells with zero regression.

   Coach application. Keep the ledger: every PASS adds a constraint row; propose rungs only against FAIL rows; treat any constraint violation as stop-and-attribute, never "the next rung might win it back".

   fixed-acceptance-matrix-per-rungpreregistered-stop-criteria-per-rung

4. doctrine-4One variable per ladder rung - counted against what the checkpoint saw

   A rung changes one variable, where "one" is counted against the checkpoint's actual training state, not against the current config's diff; batching is allowed only when each change owns a disjoint symptom space with a pre-registered ablation order.

   Case. Two rungs failed identically because resuming s1e-500 under the evolved config silently added four plant variables the checkpoint had never seen ("单变量纪律不只看「我改了什么」,还要看「checkpoint 见过什么」" - `resume-state-dr-audit`). v8 legally batched four orthogonal fixes with a written ablation order (`orthogonal-batch-with-ablation-order`); v9 spent one run completing a 2x2 factorial so either outcome convicted a factor (`fill-the-missing-factorial-cell`); v10b's three-way ablation wrongfully convicted the clock and had to be retried fairly.

   Coach application. Before any resume: diff cfg against the checkpoint's logged training state. Before any batch: require the symptom-ownership map and ablation order in writing.

   resume-state-dr-auditorthogonal-batch-with-ablation-orderfill-the-missing-factorial-cell

5. doctrine-5Pre-register risks, readings, and stop criteria before the ladder

   Before a ladder or risky rung, write down the known risks, the interpretation of every plausible outcome, and hit-any-one stop criteria - frozen before training, tightened when priors say results should come fast.

   Case. The C ladder opened with three numbered risks including the exact falsification condition for its own root choice; A/B arms carried "预注册读法(事后不改)" tables; a level expected to fail was run anyway for its pre-registered diagnostic value (`preregister-risks-and-fork-readings`). Stop criteria caught C4-redo rungs at +200 instead of full caps (`preregistered-stop-criteria-per-rung`); hardware sessions pre-registered per-config expected signatures and the disagreement rule "不改结论改账" (`preregistered-real-expectations`, `feasibility-accounts-lock-design-point`).

   Coach application. Refuse to open a rung without the written risk/reading/ stop block; after results, read conclusions off the pre-registered table and flag any post-hoc reinterpretation.

   preregister-risks-and-fork-readingspreregistered-stop-criteria-per-rungpreregistered-real-expectationsfeasibility-accounts-lock-design-point

6. doctrine-6Plant parameters are measured, never invented

   Every plant number carries measurement provenance: armature = N^2 x rotor inertia from no-load tests, friction split by rig and by API column, torque limits shaped by per-joint gait peaks, latency traced through the real pipeline, masses weighed - and DR bands are additive around the measured nominal, sized to the measured dispersion.

   Case. Guessed friction was 2.5x low and guessed damping 5x high (`friction-measured-not-guessed`); armature had been 0 with a 9:1 gearbox (81x reflected inertia, `armature-n2-rotor-inertia`); a uniform torque derating was "the wrong shape" vs measured peaks (`torque-limit-shape-by-measured-peaks`); the delay implementation itself was a wrong plant for a whole lineage (`latency-lerp-reverse-extrapolation`); the run design point was locked by three accounts including the tau_limit/kd speed ceiling (`feasibility-accounts-lock-design-point`); identified friction had to land in the right simulator API columns to act at all (`sim-api-friction-columns`). The recovery and one-leg lines opened with the same kind of accounts before any reward existed - a connected static path and the torque along it for an armless get-up, and the gains single support needs to be holdable at all (`get-up-feasibility-accounts-before-training`, `single-support-gain-authority-probe`).

   Coach application. For any plant value in a config review, ask "measured how?"; reject absolute ranges with no nominal; check API column mapping and derived-asset regeneration whenever measured values land.

   friction-measured-not-guessedarmature-n2-rotor-inertiatorque-limit-shape-by-measured-peakslatency-lerp-reverse-extrapolationfeasibility-accounts-lock-design-pointsim-api-friction-columnsget-up-feasibility-accounts-before-trainingsingle-support-gain-authority-probe

7. doctrine-7Sim2sim gate before sim2real - under deployment conditions

   Every checkpoint passes a second, independently built simulator before hardware, and both the gate and the smoke loop run under the measured deployment conditions (real pipeline delay, honest contact parameters, the deployment gain/power profile).

   Case. The standing order "先sim2sim 再sim2real" (`sim2sim-gate-before-sim2real`); acceptance flipped to match hardware only under measured condim/torsional friction (`eval-plant-honesty-contact-params`); gates moved permanently to `--delay 2` after the kicking incident (`pipeline-latency-is-plant-not-dr`); and the harness itself must be audited - a frame-convention bug in the cross-sim evaluator invalidated a whole line of verdicts (`body-frame-velocity-api-audit`). The recovery line's second simulator caught a torque penalty paid for by bracing the legs together (`torque-penalty-bought-by-leg-bracing`), and a 1.8x torque disagreement between the two plants stayed binding because its one surviving explanation was never tested (`torque-disagreement-between-simulators-unresolved`).

   Coach application. Block any hardware request lacking a second-sim PASS at deployment conditions; when sim2sim and training-side metrics disagree, treat the evaluator as a suspect too.

   sim2sim-gate-before-sim2realeval-plant-honesty-contact-paramspipeline-latency-is-plant-not-drbody-frame-velocity-api-audittorque-penalty-bought-by-leg-bracingtorque-disagreement-between-simulators-unresolved

8. doctrine-8Observation honesty - the actor's inputs are a hardware contract

   The actor observes only signals the real robot produces with realistic noise; privileged truths go to the critic; history windows are estimators and must train under plant variation; rewards on quantities the actor cannot observe buy only average suppression, never closed-loop correction.

   Case. Ground-truth velocity/forces went critic-only (`observation-honesty-critic-only`); frame_hist under zero DR memorized the trainer's plant fingerprint - 0/20 transfer (`history-obs-needs-plant-variation`); world-frame yaw rewards could not teach pull-back because heading is unobservable to the actor - correction was routed to the deploy outer loop instead of breaking the contract (`reward-observability-limit`, `deploy-heading-loop-and-align-training`).

   Coach application. Audit every actor-obs element for hardware existence; require minimal plant jitter whenever history/recurrence exists; for each reward, ask "can the actor see this error?" and route correction tasks to outer loops.

   observation-honesty-critic-onlyhistory-obs-needs-plant-variationreward-observability-limitdeploy-heading-loop-and-align-training

9. doctrine-9Reward economics are audited in realized currency

   Reward design decisions are made on realized per-step magnitudes under the actual policy and command distribution: price the do-nothing optimum before adding a mode, compare achieved values to the computed ignore-floor, calibrate thresholds between measured healthy and sick distributions, and ship every new penalty with a withdrawal clause.

   Case. feet_air_time at weight 2.0 realized 0.038 vs tracking 1.2 - drag was rational (`realized-contribution-audit`); ignoring a vy command cost 28-180x less than ignoring vx until a gated tracking term was added (`reward-cost-of-ignoring-audit`, `gate-new-reward-terms-by-command`); achieved-vs-floor separated "never learned" from "priced out" (`ignore-floor-diagnosis`); the foot-distance wall was placed between measured healthy (0.6% tax) and sick (55%) policies (`calibrate-threshold-between-healthy-and-sick`); the landing penalty carried a pre-registered stand-down condition and actually stood down (`calibration-threshold-with-withdrawal-clause`); two clearance terms were inert until zero-points and gate occupancy were checked (`inert-reward-term-audit`). A get-up policy sat because three gated terms paid the seated pose 84% of the return and the one term that could tell sitting from standing was an exp kernel reading 4.6e-5 at the real error (`seated-basin-dead-exp-kernel`); a torque-tail term was weighted by its measured steady value beside a peer term after the estimate proved 12x off (`tail-torque-needs-hinge-on-computed-demand`).

   Coach application. Never discuss weights in the abstract: demand the realized-contribution table, the ignore-floor number, and the healthy-pay calibration before any reward edit is approved.

   realized-contribution-auditreward-cost-of-ignoring-auditgate-new-reward-terms-by-commandignore-floor-diagnosiscalibrate-threshold-between-healthy-and-sickcalibration-threshold-with-withdrawal-clauseinert-reward-term-auditseated-basin-dead-exp-kerneltail-torque-needs-hinge-on-computed-demand

10. doctrine-10The zero-cost option must be the desired behavior

    For every penalty, name what the zero-cost option is; penalize failure events (slip, saturation excess, contact in flight windows), never the motion or joints that healthy behavior uses; make degenerate strategies fatal via termination where penalties cannot price them out.

    Case. Joint-usage penalties for drift taxed a 1.4%-of-momentum channel 2.7/step and collapsed training; the slip penalty costs a non-slipping gait exactly zero (`penalize-the-slip-not-the-joint`). A frozen-at-clamp joint pays zero action-rate forever - only a pre-clip saturation penalty flips the cheat economics (`saturation-cheating-zero-rate-cost`). Ungated phase shaping made standing 42x more expensive than stepping and cooked the hip motors (`moving-gate-42x-stand-tax`); crouch-shuffling lived until a height termination deleted it (`termination-closes-degenerate-basin`). A gated penalty is an exit: the policy parked just outside an uprightness gate, then just under a height gate, to stop paying a stance tax, and only a positive band plus an always-on guard closed both (`penalty-gate-is-an-escape-hatch`); a soft-limit penalty that charged the standing pose itself bought a 4.1 deg lean (`soft-limit-penalty-charges-nominal-pose`); an unpriced foot attitude was spent on edge-standing (`unpriced-foot-attitude-is-a-free-variable`); and the one-leg line listed its cheapest cheats before training and still met one through a zero-gradient band (`enumerate-cheapest-cheats-before-training`, `binary-band-reward-fake-touchdown`).

    Coach application. Run the "零代价的选项是什么" audit on every proposed term; convert motion taxes into event-conditional penalties; check the termination set against each known degenerate strategy.

    penalize-the-slip-not-the-jointsaturation-cheating-zero-rate-costmoving-gate-42x-stand-taxtermination-closes-degenerate-basinpenalty-gate-is-an-escape-hatchsoft-limit-penalty-charges-nominal-poseunpriced-foot-attitude-is-a-free-variableenumerate-cheapest-cheats-before-trainingbinary-band-reward-fake-touchdown

11. doctrine-11Measurement discipline: independent referees, signs, distributions

    A disputed measurement is adjudicated only by an independent algorithm from raw state; directional ability requires sign-antisymmetry under command reversal; bimodal metrics are reported as mode shares (never medians, never 3 seeds); ratios are not comparable when totals change; reward values compare only within one command distribution; single chaotic events never cross machines.

    Case. The triple reversal - a good metric was "refuted" by a sibling metric that shared the disease (`independent-referee-for-metric-disputes`, `body-frame-velocity-api-audit`); same-signed +/- responses were bias, not turning (`same-sign-response-is-yaw-bias`); the swing median sat in a bimodal gap (`median-hides-bimodal-distribution`); "v6 is jitterier" died on absolute energies (`ratio-metrics-need-absolute-check`); yaw gain measured 15x wrong in an oscillating frame (`heading-integral-not-body-rate`); a 44% improvement evaporated under same-distribution comparison (`same-distribution-reward-comparison`); drift direction was a limit cycle (`multiseed-sign-test-for-drift`); a cross-machine push cliff was chaos (`single-impulse-recovery-is-chaotic`).

    Coach application. Before accepting any surprising number: ask for the independent recomputation, the sign pair, the distribution shape, and the comparison conditions. Retract in writing when a metric falls.

    independent-referee-for-metric-disputesbody-frame-velocity-api-auditsame-sign-response-is-yaw-biasmedian-hides-bimodal-distributionratio-metrics-need-absolute-checkheading-integral-not-body-ratesame-distribution-reward-comparisonmultiseed-sign-test-for-driftsingle-impulse-recovery-is-chaotic

12. doctrine-12The deployment pipeline is plant

    Irreducible pipeline properties - action latency, rate limits, power/torque scaling, teleop command mappings - are part of the nominal plant, modeled from day one and reproduced in every gate; deploy-side scalings are crutches that flag unmodeled plant, and they cannot be algebraically folded into training constants.

    Case. Right-leg kicking was over-trained-delay x loop gain; power 0.8 was a gain-reduction crutch that retired when the delay was modeled (`pipeline-latency-is-plant-not-dr`); power derating damages non-forward axes first (`power-scale-hurts-nonforward-axes`); training at 0.4 scale as the "twin" of deploying 0.5 x 0.8 collapsed 0/20 (`deploy-scaling-not-training-equivalent`); one shared teleop speed sent an out-of-band lateral command and the robot clipped its own foot (`teleop-command-band-per-axis`); the latency DR range had not even covered the measured pipeline (`latency-dr-covers-measured-pipeline`). A rate limiter added at deployment only clipped a policy that kept commanding (`deploy-rate-limiter-windup`); moved into training and anchored on the last command it became an integrator in the balance loop (`slew-anchor-is-an-integrator`); anchored on the measured angle it bounded torque and kept the bandwidth (`beta-anchored-action-target`). The walking lines' safe setting, power-scale 0.8, cut the ends of the recovery policy's full-range travel and left its spikes alone; a gain inside the trained band did the job (`power-derating-cuts-full-range-contract`).

    Coach application. Demand the measured pipeline latency/limits in the plant model and in gate conditions; treat every deploy-side derating as a question ("what is this compensating?"); block per-axis command sources that exceed training bands.

    pipeline-latency-is-plant-not-drpower-scale-hurts-nonforward-axesdeploy-scaling-not-training-equivalentteleop-command-band-per-axislatency-dr-covers-measured-pipelinedeploy-rate-limiter-windupslew-anchor-is-an-integratorbeta-anchored-action-targetpower-derating-cuts-full-range-contract

13. doctrine-13DR budget is finite; its distribution is the measured support

    Robustness is a conserved budget: disturbance training on an already-hardened lineage borrows from existing margins; DR ranges span the measured deployment support - no fictitious tails (they buy degenerate gaits), no single constants (they allow thin-margin specialization); harden the plant only after the task distribution is final.

    Case. The same push dose helped a narrow lineage and damaged a balanced one - budget conservation (`push-dr-conditional-budget-conservation`); wide latency tails bought drag-glide, constant values shipped 60% thinner tilt margins - the answer is a narrow band on the measured support (`dr-tail-plant-continuation`, `constant-value-dr-overfits-margin`); task-first ordering because hardening a soon-to-change task wastes budget (`task-shaping-before-plant-hardening`); COM randomization used deliberately as a behavior-shaping tool, and rolled back on symptom per its own contract (`com-randomization-forces-leg-spread`, `com-dr-rollback-on-symptom`). DR that is switched on can still be thin: the run policy fell in the frontal plane its gain-and-latency randomization never touched (`thin-dr-judged-by-channel-coverage`), and a friction priority settled under one action contract had to be re-measured under the next (`friction-priority-re-measured-after-plant-change`).

    Coach application. Before any DR rung: check the untrained policy against the spec, the lineage's current DR load, and the measured real-world range; after it: audit retained margins, not just the new tolerance.

    push-dr-conditional-budget-conservationdr-tail-plant-continuationconstant-value-dr-overfits-margintask-shaping-before-plant-hardeningcom-randomization-forces-leg-spreadcom-dr-rollback-on-symptomthin-dr-judged-by-channel-coveragefriction-priority-re-measured-after-plant-change

14. doctrine-14Gates measure what hardware feels: posture, margins, stripped assists

    Acceptance batteries carry posture-class rows (tilt max median, per-joint L/R asymmetry, temperature) beside task rows, graded margin columns beside binary gates, chirality scored per side, at least one condition that removes the environment's free stabilization, and validated predictive scalars promoted into the gate.

    Case. Three same-shaped judging errors - survival, displacement, wz-difference - all missed what the operator felt; posture metrics had the predictive power (`task-metrics-vs-posture-metrics`, `stand-gate-posture-not-survival`); binary survival saturated and hid a 60% margin gap (`constant-value-dr-overfits-margin`); v5 passed everything on the ground and failed suspended (`suspension-probe-removes-free-stabilizer`); the hip_roll (l+r) scalar predicted real drift direction and ordering and entered the battery (`hip-roll-sum-predicts-lateral-drift`); averages hide chirality (`chirality-scored-separately`); gait-quality gates are judged at speeds that demand a gait (`low-speed-commands-reward-dragging`). The recovery line added the rest of the kit: where failed episodes end, not only where they started (`end-state-confusion-matrix`); a frozen acceptance distribution with a pinned seed (`frozen-acceptance-distribution-and-pinned-seed`); video of the metric rollout itself (`video-as-acceptance-record`); and the admission that a 10 s episode cannot see a stance that fails after a minute (`episode-length-bounds-what-a-gate-sees`). The one-leg line removed a foot-spacing wall that no gate measured, and the feet met on hardware (`removed-wall-returns-on-hardware`).

    Coach application. Review every battery for posture rows, margin columns, per-side scoring, and an assist-stripped condition; when operator feel and gates disagree, suspect the metric class first.

    task-metrics-vs-posture-metricsstand-gate-posture-not-survivalconstant-value-dr-overfits-marginsuspension-probe-removes-free-stabilizerhip-roll-sum-predicts-lateral-driftchirality-scored-separatelylow-speed-commands-reward-draggingend-state-confusion-matrixfrozen-acceptance-distribution-and-pinned-seedvideo-as-acceptance-recordepisode-length-bounds-what-a-gate-seesremoved-wall-returns-on-hardware

15. doctrine-15Fork and root selection: recoverability, maturity, frozen rewards

    Choose fork roots by which candidate's deficits the coming training can pay back (precision is recoverable; lost plasticity, symmetry, and margins are not); prefer mature checkpoints as roots even when younger ones score better as products; never fine-tune through a reward change - continuation is legal only with the reward frozen and plant/DR widening one rung at a time.

    Case. s1e-500 beat higher-precision candidates because its exclusive strengths were unrecoverable (`fork-root-recoverable-shortfall`); the b300 arm proved maturity is capital against adaptation shock (`root-maturity-vs-product-quality`); the B-arm scatter/half-recover/collapse signature falsified reward-change fine-tuning and drew the legal boundary for S2 continuation (`fine-tune-reward-change-falsified`).

    Coach application. For root debates, build the exclusive-strengths table and ask "which side can be trained back?"; require dual-arm evidence for maturity claims; classify any proposed continuation as reward-frozen or not before approving.

    fork-root-recoverable-shortfallroot-maturity-vs-product-qualityfine-tune-reward-change-falsified

16. doctrine-16Curricula: verified engagement, lineage counters, disease-phase gating

    Automatic curricula must prove they engage (a saturated ratchet is constant DR wearing a curriculum's name); every ramp counts lineage-cumulative progress, not per-process steps; penalties aimed at late-stage pathologies ramp in after exploration noise decays; difficulty rises on measured per-stratum success, never on schedule.

    Case. The s1f ratchet capped at iter 248 and never engaged (`auto-curriculum-engagement-check`); the saturation ramp re-fired at +600 after every resume and no shipped product ever saw the penalty (`curriculum-counter-lineage-steps`); the same penalty worked once gated to the disease phase and became an untouchable mechanism (`gate-penalties-to-the-disease-phase`); record-high aggregate reward hid a fully-failing delay stratum (`aggregate-metrics-mask-subgroup-failure`); bucket share is not a gradient lever (`bucket-share-is-not-a-gradient-lever`). An assist curriculum keyed to a pooled success share was withdrawn on the strength of the categories that already worked (`curriculum-criterion-conditioned-on-lagging-category`); a pace set by per-step income moved only when that income was time-gated (`per-step-income-drives-speed-time-gate`), and the same gate had to be retired in a lineage without the disease (`time-gate-vs-wide-stance-retire-the-fix`).

    Coach application. Ask every curriculum three questions: does it engage (show the internal state)? what does it count (process or lineage)? when is it present (against the pathology's phase)? Check where shipped checkpoints sit relative to every ramp.

    auto-curriculum-engagement-checkcurriculum-counter-lineage-stepsgate-penalties-to-the-disease-phaseaggregate-metrics-mask-subgroup-failurebucket-share-is-not-a-gradient-levercurriculum-criterion-conditioned-on-lagging-categoryper-step-income-drives-speed-time-gatetime-gate-vs-wide-stance-retire-the-fix

17. doctrine-17Probe before training: feasibility first, hypotheses in tables

    After two failed training attempts at a skill, stop training: demonstrate the behavior open-loop, enumerate hypotheses in a written table audited against actual configs cheapest-first, race one probe per side of the sim2real boundary for hardware-only pathologies, and use suspended tests to acquit or convict actuators before blaming authority.

    Case. "在黑暗里试钥匙" - four sidewalk rungs failed until an open-loop probe separated exploration/waveform/authority in one experiment (`open-loop-probe-before-reward-tuning`); the foot-drag mystery fell to a seven-hypothesis config audit (`hypothesis-table-code-audit`); the period-doubling was resolved by racing a reward-side and a plant-side evidence line - and both paid off, one per sub-case (`period-doubling-evidence-race`); the suspended test acquitted the roll actuator in one measurement (`suspended-test-isolates-actuator-authority`). A read-only configuration probe told a wall from a slope in the recovery line's seated basin (`configuration-probe-wall-not-slope`), and the fix it pointed to - where the feet are - took prone from 0/159 to 158/159 (`prone-dead-end-is-foot-placement`); a knob that did not move its variable was recorded as no test of the idea (`dof-vel-penalty-is-not-a-pacing-knob`).

    Coach application. When a skill resists training, prescribe the probe before any further reward edits; require verified target trajectories before imitation terms; keep a falsified-fixes list so closed roads stay closed (`amplitude-cut-falsified-yaw-fix`).

    open-loop-probe-before-reward-tuninghypothesis-table-code-auditperiod-doubling-evidence-racesuspended-test-isolates-actuator-authorityconfiguration-probe-wall-not-slopeprone-dead-end-is-foot-placementdof-vel-penalty-is-not-a-pacing-knobamplitude-cut-falsified-yaw-fix

18. doctrine-18External advice is recomputed locally; values transfer as ratios

    Every external suggestion is classified adopt / already-have / modify / trap by recomputing its claim on the local reward table and probe data; numeric values transfer only as dimensionless ratios (to tracking weight, leg length, sqrt(gL), control rate); citations are verified to exist.

    Case. "Start vy very small" would have destroyed sidewalk learning on this reward table - the gradient scales quadratically (`external-advice-audit-against-own-arithmetic`); swing-height targets and weights transferred correctly only through leg-length and tracking-ratio scaling (`transfer-ratios-not-absolutes`); the "6-step delay" was refused for lacking a control rate (`latency-dr-covers-measured-pipeline`); a borrowed reference's structure was FK-verified and its amplitude re-derived from the division of labor (`reference-structure-fk-amplitude-division`); retrieval agents fabricated verbatim arXiv quotes - only source-verifiable material was used; and one dismissed suggestion later proved right for a different mechanism, and was credited (`cycle-average-tracking-for-gait-quantities`). An advisor's staged state machine turned out to exist in none of the three papers it cited, and reading them changed the plan (`advisor-paraphrase-vs-paper`).

    Coach application. Intercept every "paper X does Y" with the local recomputation; convert absolutes to ratios before comparison; verify quotes; revisit dismissed advice when new mechanisms appear.

    external-advice-audit-against-own-arithmetictransfer-ratios-not-absoluteslatency-dr-covers-measured-pipelinereference-structure-fk-amplitude-divisioncycle-average-tracking-for-gait-quantitiesadvisor-paraphrase-vs-paper

19. doctrine-19Hardware sessions are scripted experiments, not tuning sessions

    Real-robot time executes a pre-registered matrix: risk-ordered (baseline first, fragile last with a spotter), stage-gated (suspended smoke before ground), A/B sessions bracketed by a repeated reference run, operators briefed on measured zero-command and untrained-axis behavior, chirality-aware disturbance protocols, no field tuning - the only legal field changes are scripted, single-variable, and self-reversing.

    Case. The S2 acceptance sheet (`risk-ordered-real-deployment`, `battery-bracketed-real-ab`, `know-zero-command-behavior`, `push-test-chirality-protocol`, `no-field-tuning-protocol`); the RAM-only torque experiment with automatic power-cycle rollback (`reversible-single-variable-field-experiments`); and the sim-veto rule - even sim's condemnations get one safeguarded hardware check when they judge the purpose-built configuration (`sim-veto-needs-real-confirmation`). The recovery line's first real run went ahead with its preconditions unmet and was stopped as dangerous (`first-real-get-up-violent-stage-one-policy`); after it: a staged hang, mat and floor protocol (`staged-hang-mat-floor-for-get-up`), a fixed power-cycle pre-flight and two-machine discipline (`power-cycle-preflight`, `two-machine-config-discipline`), a fall guard replaced rather than switched off (`fall-guard-becomes-a-state`), and logs that are part of the run (`hardware-log-is-the-attribution-input`).

    Coach application. Turn every hardware request into a runbook with order, gates, brackets, briefing, and anomaly plays; refuse improvised parameter changes on the floor.

    risk-ordered-real-deploymentbattery-bracketed-real-abknow-zero-command-behaviorpush-test-chirality-protocolno-field-tuning-protocolreversible-single-variable-field-experimentssim-veto-needs-real-confirmationfirst-real-get-up-violent-stage-one-policystaged-hang-mat-floor-for-get-uppower-cycle-preflighttwo-machine-config-disciplinefall-guard-becomes-a-statehardware-log-is-the-attribution-input

20. doctrine-20Close questions in writing; restart when the debt is structural

    Audited questions get frozen verdicts with citable wording and an explicit reopening bar; hardware verdicts are dated by deployment-stack and calibration state and expire when those change; and when successive rungs shuffle symptoms without net progress, freeze the lineage as regression baselines, pay the structural debts, and retrain minimal - carrying laws and instruments, not weights.

    Case. The chirality and COM questions were closed with frozen wording and "no reopening without new hard evidence" (`frozen-verdicts-semantic-boundaries`); v5/v6's condemnations expired with the deploy stack (`stale-verdicts-under-old-stack`); a 2-degree calibration fix moved the whole runnable envelope (`zero-offset-calibration-shifts-envelope`); plant upgrades are era boundaries with paired re-baselining (`plant-swap-invariants-vs-shifts`); and the 2026-08-05 reset froze v5-v11, fixed the latency FIFO / manifest / sampling / reward-table debts, and restarted - producing the lineage that reached hardware SOTA (`freeze-lineage-fix-structure-restart`, `minimal-reward-table-with-provenance`). The recovery line's real-robot verdicts ended up in three places that disagree, one of them an undated note in a command file (`write-hardware-verdicts-back`).

    Coach application. Maintain the closed-questions ledger and quote it when symptoms recur; stamp verdicts with stack/calibration versions; when a team is three rungs into symptom-shuffling, raise the restart question explicitly with the freeze-fix-restart pattern.

    frozen-verdicts-semantic-boundariesstale-verdicts-under-old-stackzero-offset-calibration-shifts-envelopeplant-swap-invariants-vs-shiftsfreeze-lineage-fix-structure-restartminimal-reward-table-with-provenancewrite-hardware-verdicts-back

21. doctrine-21Name the quantity in the space it lives in

    A goal, reward term or acceptance criterion about the feet, the base or the contact state is computed from the quantity itself - world poses, forces, per-category outcomes - never through a joint-angle, single-signal or pooled stand-in that assumes everything else sits at nominal; and every detector is validated on a behaviour known not to contain the event before it becomes a gate.

    Case. The recovery line was caught three times: |ankle roll| as "flat feet" sold stance width and the real robot slid into the splits, a hip-roll criterion was confounded by 50 deg of yaw, and the joint table said 0.271 m where the feet were 0.159 m apart; task-space terms produced the first flat, wide stance (`joint-space-proxy-for-task-space-quantity`). Flight detection lied in both directions across two lines - foot height flagged 40% false flight on a walking gait, contact force alone flagged slip chatter as hops (`contact-detector-single-signal-lies`). A pooled height average described a robot that did not exist - six in ten standing, four in ten sitting (`zero-partial-credit-is-not-an-iteration-problem`) - and the walking line had learned the same lesson on yaw rate (`heading-integral-not-body-rate`).

    Coach application. For every reward term and gate row, ask what physical quantity it stands for and whether it is measured directly; flag joint-space or single-signal stand-ins for task-space goals, ask for a detector validated on a negative control, and split pooled metrics by category before reading them.

    joint-space-proxy-for-task-space-quantitycontact-detector-single-signal-lieszero-partial-credit-is-not-an-iteration-problemheading-integral-not-body-rate

22. doctrine-22Continuation needs a live gradient; a release is chosen by a scan

    Continue a converged policy only on a change that creates a live gradient, on a short budget, with every checkpoint scanned on the transfer axis; choose a release by running the full battery over a band of checkpoints and stop on signals, never by taking the last one; and when edits to the terminal phase cannot move a behaviour, roll back and retrain with the constraint present from the start, keeping the order in which the lineage acquired its mechanisms as explicit curriculum phases.

    Case. A continuation with no new gradient drifted MuJoCo transfer from 100/98% to 80/28% while every Isaac gate stayed perfect, and a live-gradient continuation at the same depth kept it (`converged-continuation-is-poison`). One-leg checkpoints 100 iterations apart failed 1 and 38 of 40 cells, and late ones degraded (`checkpoint-choice-is-a-full-gate-scan`). Four in-lineage stance fixes failed because the stance was the end of the get-up path, and from scratch it grew right (`stance-decided-by-get-up-path`); fixes stacked on degraded states were rolled back by the user (`stop-stacking-roll-back-and-audit`); and the lineage's final recipe, trained from scratch in one run, sat at 0% because the order of its curriculum was part of the product (`curriculum-history-is-part-of-the-product`). The omni line's short adaptation budgets and mature roots are the same law seen from the other side (`continuation-budget-not-from-zero`, `root-maturity-vs-product-quality`).

    Coach application. Before approving a continuation, ask for the new gradient, the budget and the transfer axis in the scan; before approving a release, ask for the scan; after three rungs without progress on the target, propose rolling back to the last good checkpoint and a from-scratch phase plan instead of a fourth patch.

    converged-continuation-is-poisoncheckpoint-choice-is-a-full-gate-scanstance-decided-by-get-up-pathstop-stacking-roll-back-and-auditcurriculum-history-is-part-of-the-productcontinuation-budget-not-from-zeroroot-maturity-vs-product-quality

Experience cards

100 cards matching “zero-offset-calibration-shifts-envelope”.

• A single run's drift direction may be a limit cycle, not a policy bias - check the sign distribution across seedsmultiseed-sign-test-for-drift

  Mechanism understoodwalksim-evalmeasurementattributiongate-battery

  Distinguish "bias" from "broken symmetry limit cycle" by the sign distribution over many seeds; report drift as (mean, sign split), and never compare single-run drift magnitudes across versions.

  Symptom

  Net yaw over 15 s appeared to worsen from -41 deg (v2) to -84 deg (v4), inviting the conclusion that the new version drifted more.

  Context

  The Isaac-side view across 32 environments told a different story: per-env yaw was mixed-sign (20 negative / 12 positive) with mean ~0 - the drift is a limit cycle whose direction depends on initial conditions, not a systematic policy bias. The single MuJoCo run had sampled one draw from that distribution, so its magnitude could not be compared across versions as if it were a property.

  Change

  Evaluation rule: before classifying drift as systematic, run multiple seeds and examine the sign distribution; single-trajectory drift magnitudes are samples, not properties.

  Outcome

  The v2-vs-v4 drift "regression" was reclassified as not-established; later drift work (hip_roll l+r bias) used cross-policy, cross-seed evidence instead.

  Mechanism

  Symmetric dynamical systems can settle into either of two mirrored limit cycles; the selected cycle is decided by noise and initial state. A statistic whose sign is initial-condition-dependent has no meaning as a single sample - only its distribution does.

  Applies when

  • comparing heading drift or lateral drift across policy versions
  • a symmetric-looking behavior shows a consistent direction in one run
  • deciding whether to fix "drift" in reward or calibration

  “偏航反而变差(−41° → −84°):注意 Isaac 侧 32 env 的逐 env 偏航是正负混合(20/12)、均值 ≈0,说明这是极限环性质(方向随初值)而非策略偏置 —— MuJoCo 单次跑测到的是分布里的一个样本,不能当作系统性偏差。要判断需多种子统计。”

• Low-friction robustness traced to kd DR bandwidth, not friction training - by digging resolved params across 8 lineages, 3840 cellskd-bandwidth-mu-law-attribution

  Mechanism understoodomniattributionattributiondomain-randomizationprocess

  Attribute capability differences by tabulating every lineage's resolved training params and eliminating zero-variance and non-aligned columns first; never let an eval-side override knob serve as the explanation axis, and never write a mechanism into a law before it survives a targeted test.

  Symptom

  Lineages differed wildly in low-ground-friction survival, and the intuitive explanation - "some trained ground friction, some didn't" - was about to steer the ladder toward a ground-mu training rung.

  Context

  The attribution ran as a full parameter-vs-result cross: 8 lineages x 4 eval kd levels x 6 mu levels x 20 seeds = 3840 cells, with each lineage's RESOLVED training params dug out and compared item by item. First kill: all 8 lineages had ground mu pinned at (1.0,1.0) - zero variance - so low-mu differences cannot come from friction training at all. The only training parameter aligned with the mu score was kd DR bandwidth: narrow (<=0.24) lineages scored 19.9/19.5/19.5, wide (>=0.40) scored 17.1/15.2/14.6/14.2/12.8 - the two groups completely non-overlapping. Every rival was excluded item by item (kd center no; kp band no; COM small-beneficial non-driving; friction rung a clean double null 19.5->19.5 and 15.2->14.6; iteration count non-monotonic), and the one clean single-variable causal link confirmed it: the s2e-3 kd surgery (0.7,1.3)->(1.08,1.32) moved the score 17.1->19.5. Counter-proof against "each best at its own operating point": the narrow-band lineage evaluated OUT of band (18.2) still beat the wide-band lineage at its own band center (9.2). Two axes were ordered never to be conflated (the first attribution's own error): training kd bandwidth is a parameter axis / lineage property; the eval-side --kd-scale knob is a plant axis (more damping physically helps on slippery floors for ALL policies) - "plant 轴只能当部署缓解,不能当 归因". A tempting mechanism story ("drag vs step attractor") was tested and falsified, and explicitly kept OUT of the law: "机制未定, 不入定律".

  Change

  The planned ground-mu training rung was recommended closed ("建议 不开") in favor of a kd band-narrowing rung (0.8,1.2)->(0.9,1.1) centered on the deployed value - with a pre-registered risk that the law demands "bandwidth = measured dispersion" and the real robot's kd dispersion was not yet measured; if it exceeds +/-10%, narrowing sacrifices real coverage and the rung must yield.

  Outcome

  A whole training rung was deleted from the ladder by attribution alone (the second S2 pass dropped mu and push, 5 rungs -> 3); floor material became a deployment-selection input (mu <~0.6 -> deploy the kd1.2 gain profile) rather than a training target.

  Mechanism

  Cross-lineage performance differences must be attributed over the actual training-parameter table, not over eval knobs or plausible stories: eval knobs act on the plant for every policy (a physical effect), while lineage properties come only from training-time parameters. Zero-variance columns are free eliminations, and one clean single-variable rung is worth more than any correlation.

  Applies when

  • explaining why lineages differ on a robustness axis
  • an eval-side knob (gain scale, power) changes results and invites misattribution
  • deciding whether to open a DR rung for an axis never actually varied in training

  “8 血统地面 μ 训练带全部钉 (1.0,1.0) 零方差,低 μ 差异与「训没训地面摩擦」无关,是 kd DR 带宽的副产物 … 宽 ≤0.24 → 19.9/19.5/19.5;宽 ≥0.40 → 17.1/15.2/14.6/14.2/12.8, 两组完全不重叠。… 训练 kd 带宽 = 参数轴/血统属性;评测部署 --kd-scale = plant 轴 … plant 轴只能当部署缓解, 不能当归因。… 机制未定, 不入定律。”

• Mirror augmentation over an asymmetric default injects systematic error - symmetrize the default first and verify the transform bit-exactmirror-augmentation-needs-symmetric-default

  Mechanism understoodwalkobservation-designobservation-honestycurriculumprocess

  Before enabling any symmetry augmentation, make every constant inside the observation encoding exactly symmetric, and validate the mirror transform against forward kinematics to machine precision - an unverified augmentation is a new error source, not a regularizer.

  Symptom

  Mirror data augmentation was about to be added while both default poses (standing_pose, walk nominal_pose) were asymmetric - stale hand-tuned compensations from before a ground re-calibration, with hip_yaw differing 2.40 deg between sides and the foot soles actually tilted (pitch 2.88/1.35 deg, roll -2.47/+0.25 deg).

  Context

  The observation encodes joint_pos_rel = q - default. Under mirroring q_l -> -q_r, the relation (q-default)_l -> -(q-default)_r holds only if default_l = -default_r; with an asymmetric default, augmentation produces observation pairs that are NOT mirror images, i.e. "default 不对称时做镜像增强会引入系统性错误,比不做还糟" (worse than not doing it). The fix: adopt model geometric zero as standing default (MuJoCo FK verified: sole pitch/roll exactly 0, asymmetry 0.00 deg) and a symmetric crouch for walk (hip -0.25/knee -0.5/ankle -0.25 satisfying hip - knee + ankle = 0 to keep soles flat). The mirror transform itself was verified bit-exact before use: pseudovector vs polar-vector sign patterns (ang vel [-1,1,-1], gravity [1,-1,1], cmd [1,-1,-1]), joint swap-and-negate; FK check that left-foot pose under q equals the mirror of right-foot pose under mirror(q), measured error 0.00e+00.

  Change

  Defaults symmetrized first (with init heights recomputed by FK), stand policy retrained on the new default so both policies share one default; augmentation enabled only after the FK mirror test passed.

  Outcome

  stand_v1 achieved exact left/right pairing (l_knee -0.1013 / r_knee +0.1013), six-pair asymmetry 0.0 deg, height fluctuation 7 -> 1 mm, 33% less mean |action|.

  Mechanism

  Augmentation asserts an equivariance of the observation encoding; any asymmetric constant inside the encoding (the default) breaks the asserted symmetry, so the augmented data teaches a false invariance. Verifying the transform against FK geometry tests the assertion end to end, independent of the training stack.

  Applies when

  • adding mirror/symmetry augmentation to locomotion training
  • defaults or trims were hand-tuned per side at any point
  • observations are expressed relative to a default pose

  “观测里 joint_pos_rel = q − default。镜像下 q_l → −q_r,要让 (q−default)_l → −(q−default)_r 成立,必须 default_l = −default_r。default 不对称时做镜像增强会引入系统性错误,比不做还糟。… 位置误差与姿态矩阵误差实测均为 0.00e+00。”

• Order hardware runs by sim risk, gate each stage on the last, and put the fragile cell last with a spotterrisk-ordered-real-deployment

  Replicatedomnireal-deployreal-acceptanceprocess

  Script hardware sessions as a risk ladder: baseline first, sim-riskiest last with a spotter, suspended smoke before ground, each stage gated on the previous, environment (floor mu) recorded as a selection input - and stop at the stage that misbehaves.

  Symptom

  Five policy-x-gain combinations had to go on hardware in one session, with sim survival ranging from 20/20 down to 17/20 (and zero-command survival down to 2/20) - an unordered session risks breaking the robot on an avoidable run.

  Context

  The execution sheet fixed the order as sim-risk low to high, control baseline first (current SOTA establishes the floor reference), the fragile cell (fric-2400@kd1.0) last with a person spotting throughout. Stage gating: suspended smoke (feet off ground, 10 s each, all five pass before anything touches down) -> suspended with IMU and forward command (gait forms in the air) -> grounded runs -> speed raise only for combos that survived the previous stage -> zero-command tests only with a spotter, ordered by sim zero-cmd survival, with the 2/20 cell skipped by default. Preconditions include recording the floor material and estimating mu (if mu <~0.6, sim says pick the kd1.2 gain as main), port/CAN self-check, calibration frozen. Any stage failing stops the session at that stage: "任一段出问题就停在那一段, 不要跳到下一段".

  Change

  Session structured as a risk ladder with per-stage gates instead of a flat checklist; per-combo sim survival numbers written into the run table as the ordering key.

  Outcome

  The session design localized any failure to the cheapest stage that could reveal it, kept the robot safe for the informative fragile run, and made the control baseline available before any comparison run.

  Mechanism

  Hardware sessions consume a shared budget (robot integrity, battery, floor time); ordering by predicted risk means information is bought cheapest-first, and stage gates convert an expensive failure into a cheap earlier one. Baselines run first because every later reading is relative to them.

  Applies when

  • taking multiple policies/configs to hardware in one session
  • a candidate is known-fragile in sim but must be measured
  • writing a deployment runbook for a new robot

  “跑序 = sim 风险从低到高, 最险的放最后 (依据 = 存活门/零指令存活) … ⑤ 是 sim 里最脆的一格 … 放最后跑, 全程留人扶, 起步即给 cmd, 零指令不做。… 任一段出问题就停在那一段, 不要跳到下一段。”

• A quadratic clearance reward stalled for 3500 iters near target - switch to an indicator on accumulated heightindicator-reward-avoids-gradient-decay

  Mechanism understoodwalkreward-shapingreward-shaping

  When a shaped term plateaus near its target, check the gradient profile: replace vanishing-gradient forms with threshold/indicator forms for the final approach, and prefer delta-accumulation over absolute positions to immunize against frame offsets.

  Symptom

  The quadratic-error clearance term froze at -0.002 from iteration 2000 to 5500 - thousands of iterations with no progress on foot lift.

  Context

  Diagnosis: a quadratic penalty's gradient vanishes as the error approaches target, so exactly where the last millimeters must be earned the incentive fades to nothing. The replacement (Humanoid-Gym form): accumulate the swing-phase height climb per foot, reward a BINARY indicator |accumulated - target| < 0.01 masked to the planned swing window, reset on contact, weight +1.6 as a positive reward. Two properties: the indicator's incentive is constant until the threshold is crossed (no decay zone), and accumulating height DELTAS makes any constant sole-frame offset cancel automatically - which structurally sidesteps the earlier 0.0585 m zero-point bug ("顺带绕开我先前那个'忘了减 0.0585 导致惩罚恒为 0'的坑").

  Change

  Clearance reformulated from quadratic penalty on instantaneous height to indicator on per-swing accumulated climb (target 0.03 m by leg-length scaling, weight +1.6).

  Outcome

  Part of the v5 package under which lift finally moved (v5 29 mm, v6 34 mm vs the stalled 18-24 mm era); the offset-cancellation property removed one whole bug class from the term.

  Mechanism

  Policy-gradient learning follows the reward's local slope; quadratic shaping concentrates slope far from target and starves it near target, so convergence stalls precisely at the finish line. An indicator pays a constant bounty until the goal is met; formulating on deltas rather than absolutes removes sensitivity to reference- frame constants.

  Applies when

  • a reward term's value freezes short of target for thousands of iters
  • designing clearance/height/precision terms
  • reward code depends on absolute link positions

  “现行二次型在接近 target 时梯度趋零 —— 这正是 clearance 从 iter 2000 到 5500 卡在 −0.002 不动的原因。… 二值指示在跨过阈值前梯度恒定,没有衰减区 … 累积 delta 让 SOLE_OFFSET 自动抵消”

• Three times a joint-angle stand-in for a foot-level quantity was gamed or lied - the absolute ankle roll sold stance width to buy flat feet, a hip-roll criterion was confounded by 50 deg of yaw, and the joint table said 0.271 m where the feet were 0.159 m apartjoint-space-proxy-for-task-space-quantity

  Replicatedrecoveryreward-shapingreward-shapingmeasurementreal-acceptance

  Express foot-level (task-space) goals and acceptance criteria in task space - link attitude and lateral spacing from world poses - never through joint angles that assume other joints are at zero, and measure the task-space value before trusting a joint-space estimate of it.

  Symptom

  The line's first real-robot get-up (v2_6, 2026-08-11) was "fairly stable", but after standing the feet were too close and the robot slid into the splits and fell several times; later reports added that it also got up through a split posture.

  Context

  (1) flat_feet penalized sum |q_ankle_roll|, but a flat foot is ankle roll compensating hip roll; the proxy taxed the compensated wide solution, and the untaxed combination was hips straight plus ankles at zero - flat and narrow. On hardware the lateral support shrank to hip width plus centimetres, lateral balance rested on 11 N*m ankle motors, and the feet slid apart. (2) The next rung's acceptance criterion "hip roll >= 20 deg" assumed zero hip yaw; at 50 deg of yaw the lateral contribution is x cos 50 ~ 0.64 - the same substitution again, inside a criterion. (3) A new task-space diagnostic reading the foot links' world poses measured v2_6c's stance at 0.159 m where the kinematic audit from joint angles had said 0.271 m (0.271 x cos 47 ~ 0.17).

  Change

  Rule written into the spec: task-space quantities are never expressed through joint-space proxies. V3.1's stance terms were all task-space: flat_feet_task from the foot links' world orientation, lateral foot spacing in metres, stand_pose stripped of both roll joints.

  Outcome

  From scratch with task-space terms (V3.1 P1c): lateral stance 0.355 m, foot residual tilt median 0 deg / P75 2.0 deg, all six criteria passing, mu 1.0-0.4 all 100%.

  Mechanism

  A joint proxy bundles the goal with everything else those joints do; the optimizer finds the combination the proxy does not tax, and a joint-based criterion silently assumes the other joints sit at their nominal.

  Applies when

  • rewarding flat feet, stance width, foot placement or end-effector pose
  • an acceptance criterion is written in joint angles for a geometric goal
  • joints with large yaw or coupled axes are involved

  “**病根 = 关节空间代理**:`flat_feet` 罚 Σ|q_ankle_roll|(§41 取的简易口径)。 "脚掌平"的运动学正解是 **踝滚补偿髋滚**(q_ankle_roll ≈ −q_hip_roll) … 代理把"脚平"和"站距"绑死在一起卖了。”

• Gate a new reward term by its command so all old modes score pointwise identicalgate-new-reward-terms-by-command

  Mechanism understoodomnireward-shapingreward-shapingprocess

  When a reward term must be added mid-lineage, gate it on the condition that defines the new task so every pre-existing situation scores exactly as before - and still watch for value-rescale pathologies inside the new mode.

  Symptom

  Adding a lateral tracking reward (track_lin_vel_y_exp) ungated would have paid 0-2.0 per step even in modes with cmd_vy = 0 (healthy gait sway of vy ~0.1 already earns 1.28), shifting the whole reward table by a large bias and rescaling the value function - no longer "just adding one mode".

  Context

  C4 was the C ladder's only true reward surgery. Single-variable discipline required that the change be invisible to every existing mode. The chosen construction: gate_by_cmd=True - the term pays only when |cmd_vy| > 0.02, so for all modes with cmd_vy == 0 the term is pointwise zero, i.e. the reward is pointwise identical to before the change. The same trick appeared earlier in C1: replacing the vy L2 tax with a command-error version that is "对 cmd_vy≡0 逐点同值" (pointwise equal when cmd_vy is 0), explicitly classified as not-a-reward-change.

  Change

  track_lin_vel_y_exp added with gate_by_cmd=True (weight +2.0, std 0.15); the residual acknowledged honestly - inside the side bucket the values DO change, so the rung still watched the known reward-reshuffle pathology signature (s1c B-arm: scatter -> half-recover -> collapse) as a stop criterion.

  Outcome

  Old modes provably unaffected (pointwise-equal argument); attribution for any change in old-skill metrics stayed clean through the C4 redo series.

  Mechanism

  PPO's critic normalizes to the reward scale it sees; an ungated additive term shifts returns in every state and re-scales advantages globally, entangling the new skill with all old ones. Command-gating confines the new term's support to the new mode's state distribution, making "pointwise identical elsewhere" a provable property rather than a hope.

  Applies when

  • adding a tracking/shaping term for a new command or skill to a lineage that must not regress
  • reward change proposed while other skills are still being gated
  • reviewing whether a config diff counts as a reward change

  “只在 |cmd_vy| > 0.02 时付。不门控的话它对 cmd_vy≡0 的老模式也给 0~2.0 分（健康摇摆 vy≈0.1 → 1.28），等于给整张奖励表加一个大偏置、值函数尺度全变 … 门控后老模式逐点得 0 = 与加项前逐点同值，单变量纪律成立。… 但 side 桶内的值确实变了 —— 这仍是奖励表改版，开级盯 s1c B 臂签名”

• Before training a one-leg stand the spec named the cheapest cheats - hopping on the support foot, a raised foot resting unloaded, a leg tripod - and gave each a countermeasure and a gate; one still appeared and was caught by exactly those gatesenumerate-cheapest-cheats-before-training

  Observed onceonelegreward-shapingreward-shapinggate-battery

  Before training, list the cheapest behaviours that would satisfy each reward term without doing the task, give each a countermeasure in the reward and a gate in acceptance, and prove the intended behaviour is reachable with a probe - then treat any gate the policy games as evidence about the reward, not the gate.

  Symptom

  The literature's single-leg benchmark reports eight state-of-the-art general policies holding a clean one-leg stand 0 times out of 90 - they survive by sneaking steps and hops - so the task's first adversary was the policy's own cheating.

  Context

  The spec's shape self-check ("what is the zero-cost option?") listed, for the one-foot bucket: the cheapest cheat, a foot resting on the ground without load, countered by a 5 N contact threshold plus positive swing income; the second cheapest, small hops on the support foot to reset balance, countered by a continuous support-air penalty plus a gate of zero support-foot flight segments. The probe that preceded training had already seen a third: early low-lift postures "survived" by pressing the swing foot at 78-95 N, a leg tripod, removed by folding the shank back. The two-foot bucket was checked too: its zero-cost behaviour is ordinary standing, with no odd base state.

  Change

  Countermeasures and gates written before training: swing-contact and support-air penalties, gate 2 (zero swing-foot contact frames above 5 N), gate 3 (zero support-foot flight segments).

  Outcome

  The first run still found the unloaded-foot cheat (a binary reward band gave it no gradient to lift) - and it was caught, by the contact gates and the cross-simulator comparison, not discovered on hardware. The retrained V0 passed all gates 40/40, including zero support-foot flight after the flight detector was corrected.

  Mechanism

  A policy optimizes the reward, not the intent; the cheapest behaviours that satisfy the reward are predictable from the reward's structure, and a gate written for each before training turns a silent cheat into a failed row.

  Applies when

  • designing rewards for balance, contact or "hold still" tasks
  • benchmark policies are known to cheat the task
  • writing acceptance gates for a new skill

  “文献里 8 个 SOTA 通用策略在单脚站基准上 0/90 干净保持， 全靠偷步偷跳活命，这是本任务的第一反作弊对象 … 单脚桶下最便宜的作弊是"脚虚放地上不受力"——接触判定 >5 N 力阈（沿用），配 swing_height_band 正收入拉开。 … 第二便宜是"支撑脚小跳重置"——support_air_penalty 连续罚 + 验收门支撑脚腾空段=0 双保险。”

• A nonzero response with the same sign for + and - commands is bias, not abilitysame-sign-response-is-yaw-bias

  Replicatedomnisim-evalmeasurementgate-batteryattribution

  Before crediting any directional skill, test both command signs: response must flip sign with the command; a same-signed pair is a bias to subtract, not an ability to report.

  Symptom

  Root-selection probe showed nonzero wz "tracking percentages" on turn commands, tempting the read that candidates could partially turn.

  Context

  During C-ladder root selection, s1e-500's measured yaw rate was +0.084 rad/s for cmd +0.3 and +0.093 rad/s for cmd -0.3 - same sign both ways. The same check on the C2 baseline gave wz+0.20 -> -0.13 and wz-0.20 -> +0.12 (again same sign), while the alternative root s2e_pd-1400 gave +0.16 / -0.16 - opposite signs, i.e. a genuine 16% command response.

  Change

  Reading corrected and written into the execution sheet: percentages on directional commands are meaningless unless the +cmd and -cmd responses have opposite signs; all three candidates were re-classified as "cannot turn, cannot sidewalk - C2/C3/C4 learn from zero". Acceptance criteria thereafter required "tracking >=50% AND left/right opposite-signed".

  Outcome

  Prevented crediting turn/sidewalk ability that did not exist; the antisymmetry clause became a standing part of every turn and sidewalk PASS condition (C2, C4, C4-redo levels all carry "且左右反号").

  Mechanism

  A constant yaw (or lateral) bias projects onto any command's sign convention and shows up as fake fractional tracking; only sign-antisymmetry under command reversal distinguishes a feedback response to the command from an open-loop offset.

  Applies when

  • evaluating turn/sidewalk/any signed-command tracking percentages
  • a candidate shows partial tracking on an axis it was never trained on
  • writing PASS criteria for a new directional skill

  “C2/C3 那些非零的 wz 百分比不是转向能力 —— 转向+ 与 转向− 的实测同号（s1e：cmd +0.3 → +0.084，cmd −0.3 → +0.093 rad/s），那是恒定偏航偏置。… 三个候选都不会转、都不会侧走。”

• Record-high training reward hid a fully-failing DR subgroup - aggregate metrics average over draws, gates must test per conditionaggregate-metrics-mask-subgroup-failure

  Mechanism understoodomnitraining-rundomain-randomizationgate-batterycurriculum

  Never gate on metrics aggregated across DR draws: evaluate at fixed representative conditions (especially the deployment-critical stratum), and if a difficulty axis matters, ramp it on measured per-stratum success rather than sampling the full range from iteration zero.

  Symptom

  omni_s1e trained under constant-wide latency DR (0, 0.06 s) posted the lineage's highest-ever Isaac reward (129) - while the --delay 2 smoke evaluation showed 3/3 falls from iter 1500 onward, persisting to early stop; the usable checkpoint window shrank to iters 500-1000.

  Context

  Diagnosis written plainly: "聚合奖励掩盖重延迟尾部子群体失败" - the aggregated reward averages over latency draws, so the majority of light-delay environments can mask the total failure of the heavy-delay tail. The remedy for the training side was a survival-gated ratchet curriculum (survival_gated_latency): the sampling cap starts at 0.02 s and rises +0.01 only when a 4096-reset window's survival (time_out share) reaches >=90%, capped at 0.06, ratchet up-only - "增益与延迟耐受一起长,不升到策略撑不住的 地方" (gain and delay tolerance grow together; never raise past what the policy can hold). The detection side was already in place from the noise-crutch episode: the per-condition smoke curve, not the training reward, is the health readout.

  Change

  Latency exposure made curriculum-gated on measured subgroup survival instead of uniform-from-zero; per-condition (--delay 2) smoke evaluation kept as the authoritative curve; watcher scoring adjusted (survival weighted 3x) so recovery during hard phases is not early-stopped away.

  Outcome

  The failure mode was caught by the smoke curve within one generation; the follow-up redesign (deterministic staged latency) superseded the ratchet, but the aggregate-masking lesson held through both.

  Mechanism

  Expected-return training weights each DR draw by probability, so a subgroup can contribute bounded loss while being catastrophically failed; any scalar averaged over the randomization cannot distinguish "uniformly decent" from "great on easy draws, dead on hard ones". Only conditioning the evaluation on the stratum reveals the split, and curricula should raise difficulty on measured stratum success, not on schedule.

  Applies when

  • training reward hits records while a fixed-condition eval degrades
  • wide DR on an axis where deployment sits at one known value
  • designing curricula for difficulty axes (delay, push, terrain)

  “常量 latency DR (0,0.06) 从零训被证伪——Isaac reward 129 历代最高,但 --delay 2 冒烟 iter1500 起 3/3 全摔持续到早停(聚合奖励掩盖重延迟尾部子群体失败,可用窗口只剩 500/1000)。… 采样上限 0.02 起步 … ≥90% 才 +0.01s,0.06 封顶,棘轮只升不降。”

• Ideal PD is not enough - add a delay buffer and fit armature/friction/delay per jointactuator-delay-buffer-fitting

  Observed oncewalkactuator-modelingactuator-modelingplant-calibration

  Never ship ideal PD to hardware: add a measured delay (in control steps) and per-joint armature/friction fitted from step and sine responses, and treat remaining actuator mismatch as your standing largest sim2real residual.

  Symptom

  Standard ideal PD actuator model transfers poorly; sim assumes targets take effect instantly and joints reach arbitrary acceleration.

  Context

  A developer with a successful on-hardware Isaac Lab biped modified the actuator model in two ways and calibrated it against the real robot: step-response plus sine-sweep tests (positive step, negative step, sine tracking), overlaying sim curves on measured curves and hand-tuning.

  Change

  (1) Delay buffer: action targets take effect after a uniform 6 time-step delay on all joints; (2) acceleration limiting so the actuator cannot reach arbitrary acceleration; (3) per-joint fit of armature / friction / delay - different joints genuinely needed different values.

  Outcome

  Hip joints fit worst, knee best; the developer rated the result "not perfect, the best I could do" and still listed actuator-model improvement as next work - i.e. even the fitted model remained the dominant residual.

  Mechanism

  Real actuation is a lagged, bandwidth-limited system; a delay buffer and acceleration cap are the two cheapest structures that reproduce its phase and magnitude response. Per-joint differences come from differing load, wiring, and friction states, so a single global constant underfits.

  Applies when

  • actuator model in sim is ideal PD with no delay
  • step-response of real joint visibly lags or overshoots the sim's
  • budgeting which sim2real gap to attack first

  “标准 ideal PD actuator 不够用，他改了两处：延迟缓冲：目标不是立即生效，全部关节统一 6 个 time step 延迟 / 加速度曲线：执行器不能瞬间达到任意加速度 … 用 armature / friction / delay 三个参数逐关节拟合，标定方法是阶跃响应 + 正弦扫描 … 髋部关节偏差最大，膝关节最好。”

• Run acceptance under measured contact parameters - honest condim/torsional-friction flipped a false PASS into a real-matching FAILeval-plant-honesty-contact-params

  Mechanism understoodwalksim2sim-gatesim2simplant-calibrationgate-battery

  Pin the evaluation plant's contact model to measured values (contact dimension, torsional/rolling friction, mu) before trusting any gate that involves slip, impact, or drift - a gate can only fail a policy for physics its simulator contains.

  Symptom

  walk_v5 passed the old acceptance battery yet failed on the real robot (footfall force, drift, kicking) - the evaluation plant was flattering the policy.

  Context

  The battery was re-run under "honest contact parameters" - condim 4 (adding torsional contact) with measured torsional friction 0.035 - and v5 then FAILED exactly the rows corresponding to its real problems: heading 185 deg (limit 30), support-foot yaw slip 284 deg (limit 80), landing force 1.72x (limit 1.5x), suspended tilt 45.9 deg (limit 10). The slip physics depends on torsional friction, which the default contact model (condim 3) does not even simulate - a slip problem is invisible to an evaluator that cannot represent yaw friction at the foot. Term-sizing measurements for the new rewards were likewise taken under the same honest parameters (cmd 0.45, skipping the 5 s start transient).

  Change

  Acceptance harness pinned to condim 4 / torsion 0.035 (measured); verdicts issued under defaults declared non-citable for these rows.

  Outcome

  Sim acceptance verdicts began agreeing with hardware ("现在失败, 与真机一致"); the v6 fixes could be developed and validated against an evaluator that could actually see the disease.

  Mechanism

  An evaluator is a plant model too: contact dimensionality and friction values decide which failure modes exist in the simulation at all. Evaluating under default contact parameters tests the policy in a world where its real failure is physically impossible, producing structurally false PASSes.

  Applies when

  • sim acceptance passes policies that fail on hardware
  • slip/drift/impact gates run under default simulator contact settings
  • setting up a cross-simulator evaluation harness

  “accept_v2.py 已加三条判据, walk_v5 在诚实的接触参数下(--condim 4 --torsion 0.035)现在失败, 与真机一致:直行 15s 航向累计 <30° | 185° ✗ … 落脚力峰值 <1.5× 体重 | 1.72× ✗”

• The hip_roll (l+r) asymmetry scalar predicted real-robot lateral drift - promote validated sim scalars into the gatehip-roll-sum-predicts-lateral-drift

  Replicatedomnireal-acceptancereal-acceptancegate-batterysim2sim

  Hunt for cheap sim scalars that predict real-robot behaviors, validate them on direction AND ordering across multiple policies, then promote them into the acceptance battery; treat later violations as debt to justify in writing, not noise to ignore.

  Symptom

  A persistent hip_roll left/right asymmetry row in the sim2sim symmetry table had been dismissed as "calibration or mechanical asymmetry" noise; meanwhile real deployments drifted sideways by policy-dependent amounts.

  Context

  Forward-kinematics analysis reframed the scalar: both hip_rolls move the feet in +y for positive angle, so a same-signed (l+r) sum IS a lateral translation mode - the scalar is a direct lateral-drift bias estimate. Checked against real deployments: s1e (l+r = -0.0178, smallest magnitude) was the steadiest with least drift; 700 (+0.0253) drifted mildly left; A800 (+0.0267) drifted clearly left with the largest tilt 12.9 deg. Direction correct 3/3, ordering correct 3/3 (the log's heading calls it "四枚四中", four-for-four).

  Change

  The scalar was promoted into the acceptance battery as a posture-class criterion alongside tilt-max median: "hip_roll 左右不对称 |l+r| 不得比父代大" - doubling as a heat proxy (error ~ torque ~ heating).

  Outcome

  Used at every later gate; when the C4 product exceeded it by +0.005 rad (~+0.3 deg vs parent), the criterion was not silently waived - it was booked as explicit debt with a mechanism argument (the increment is task-required, far smaller than the sidewalk amplitude +/-2.2 deg) plus a related account (stand saturation 32.4% -> 37.2%).

  Mechanism

  A policy's static joint-angle bias in a translation-producing mode integrates into real-world drift; sim can measure that bias precisely and cheaply. A sim scalar earns gate status exactly when its predictions are validated against hardware in both direction and ordering - and a validated gate may only be exceeded with a written mechanism-level justification, never silently.

  Conflicts

  The log's heading says "四枚四中" (4/4) but the evidence table lists three policies and the text says "方向 3/3、排序 3/3"; the fourth instance is not shown in this file.

  Applies when

  • a real robot drifts or leans in a policy-dependent way
  • deciding which sim measurements deserve gate status
  • a validated gate criterion is marginally exceeded by a new product

  “s1e | −0.0178（绝对值最小）| 微右、最不飘 | 三者中最稳、飘最小 ✓ … A800 | +0.0267 | 左、最飘 | 明显左飘、倾角最大 12.9° ✓ 方向 3/3、排序 3/3。 → 正式纳入验收表（与「倾角 max 中位」并列为姿态类判据）。”

• The action-delay was implemented as lerp - beyond one step it extrapolated BACKWARD, so a whole lineage trained on a fictitious actuatorlatency-lerp-reverse-extrapolation

  Mechanism understoodinfraactuator-modelingactuator-modelingplant-calibrationsim2sim

  Unit-test plant-model code (delays, filters, randomizers) against hand-computed truth across its FULL configured range, not just the nominal case - a delay must be a queue, and any interpolation used outside [0,1] is a silent plant corruption that training will faithfully absorb.

  Symptom

  Every walk/stand model up to v11 had been trained on a silently wrong plant: the action-latency implementation lerp(cur, prev, lag) is only an interpolation for lag <= 1 - at lag 3 it computes 3*prev - 2*cur, a REVERSE extrapolation. With the configured (0, 0.06) s at 50 Hz (lag in [0,3]), about 2/3 of environments were adapting to actuator dynamics that do not exist.

  Context

  Listed as evidence item #1 for the full restart: "全部旧模型训在错误 plant 上" and the head suspect for the real robot's wild kicking. The fix replaced it with a true FIFO delay line (commit 7f13793) plus its own regression test (tests/test_action_latency.py) - but every exported ONNX predated the fix, which is part of why the lineage was frozen rather than patched.

  Change

  Delay implementation rewritten as an honest FIFO with unit tests; the restart baseline trained on the corrected plant from day one.

  Outcome

  A generation-scale training investment was revealed to have a corrupt plant underneath; the class of bug (plausible-looking math that silently changes meaning outside its valid range) got a permanent test.

  Mechanism

  lerp(a, b, w) leaves the segment for w > 1; used as a delay it fabricates high-gain inverted dynamics precisely in the largest-delay draws, so the policy learns compensation for an actuator that cannot exist - and DR then trains robustness to the artifact rather than to reality. No training metric can catch this: the sim is self-consistent, just wrong.

  Applies when

  • implementing or auditing action delay / filtering in a trainer
  • a lineage behaves as if compensating dynamics nobody modeled
  • deciding whether old checkpoints are salvageable after a plant bug

  “动作延迟旧实现 lerp(cur, prev, lag) 在 lag>1 时是反向外插(w=3 → 3·prev−2·cur),配置 [0,0.06]s@50Hz 即 lag∈[0,3],约 2/3 env 在适应不存在的执行器动态。7f13793 已换真 FIFO,但所有 ONNX 均训于修复之前 —— 真机"乱踢"的头号嫌疑。”

• Fix a too-deep nominal pose before adding any penalties - the default stance defines the basin training starts innominal-posture-before-penalties

  Mechanism understoodwalkreward-shapingreward-shapingplant-calibration

  Before tuning penalties on a degenerate gait, audit the nominal pose and height targets against morphology and published ratios; if the default stance encodes the degenerate behavior, fix it first - and recompute dependent quantities (init height) by FK, not by hand.

  Symptom

  Policy lived in a crouched shuffle; nominal knee angle was 0.5 rad (28.6 deg) - deeper than published configs (Unitree G1 0.3 rad / 17.2 deg, Booster T1 0.4 rad) - so the policy's starting point and its action-space center both sat inside the crouch basin.

  Context

  Initially ranked "secondary" in the local diagnosis, this was promoted to co-first priority by the cross-check against published reward tables, which states that with nominal knee flexion above ~0.4 rad, fixing the posture must precede adding any penalties ("改这个之前别加任何 惩罚都是白费"). Companion base-height items: walk profile had weakened base_height_l2 to -5.0 (base class -10, field standard -10 to -20, "the second most common cause of death"), and the height target must be the STANDING height (0.384), not the crouch height.

  Change

  Nominal knee 0.5 -> 0.3 rad with init_base_height recomputed by MuJoCo FK (0.3739 -> 0.3802); base_height_l2 restored to -10 with standing height target; both bundled as first-priority alongside the clearance term.

  Outcome

  Part of the v5/v6 package that lifted swing height to 34 mm and tracking to 87%; the crouch basin stopped being the default answer.

  Mechanism

  The nominal pose is the fixed point every regularizer pulls toward and the point where action=0 lands; if that point is itself the degenerate posture, every penalty fights the geometry. Correcting the attractor is prior to shaping the gradient field around it.

  Applies when

  • policy converges to a crouched or collapsed posture
  • nominal joint angles were chosen for stability rather than gait
  • base-height reward targets or weights were locally weakened

  “研究明确说"nominal 膝屈超过 ~0.4 rad 必须先改,改这个之前别加任何惩罚"。我们是 0.50,超标。… base_height_l2 在 walk profile 里被减到 −5.0(基类是 −10)。研究说这是"第二常见死因"且应 −10 ~ −20。改回 −10。目标高度用站立高 0.384 是对的(研究要求 target 必须是*站立*高度而非蹲姿)。”

• Real robot walked at half the sim clock for two generations - resolved by racing a reward-side and a plant-side evidence line, not by guessingperiod-doubling-evidence-race

  Observed oncewalkattributionattributionactuator-modelingplant-calibrationprocess

  For a hardware-only pathology, refuse to guess: pre-register one probe per side of the sim2real boundary (can the reward mechanism change it on hardware? can fitted plant parameters reproduce it in sim?) and let the first positive result direct the next version.

  Symptom

  The number-one sim2real gap: on hardware v6/v7 stepped at 1.23-1.32 Hz - almost exactly half the 2.50 Hz gait clock they were trained and simulated at; sim never reproduced it, two generations running.

  Context

  Instead of committing training budget to a guess, v8 pre-registered two mutually controlled evidence lines and kept the clock OUT of the training variables: (a) reward-side - if the v8 saturation fix revives joint_pos_ref (the term that pins the gait to the clock), re-run hardware and see whether frequency returns to 2.5 Hz (hypothesis: v7's frozen actions meant NO reward was pinning the gait to the clock, and the real plant - with armature and friction making high frequencies expensive - slid down to the leg's pendulum natural frequency ~1.1 Hz); (b) plant-side - record suspended joint data (fit_actuator), fit armature/friction, load the fitted values into sim2sim and see whether the 1.25 Hz reproduces IN SIM. Decision rule fixed in advance: "谁先给出阳性结果谁定 v9 的方向 (奖励侧 vs plant 侧)" - whichever line goes positive first sets the next version's direction.

  Change

  Period-doubling excluded from the v8 change set; both diagnostic lines scheduled in parallel as non-blocking work; frequency reported factually in acceptance with no pass/fail attached ("倍周期是否消失 不设判定,它是 §9 的关键证据").

  Outcome

  The gap was routed into a decisive-experiment structure rather than a speculative retrain; the plant-side line pointed at exactly the unmodeled armature/friction that were later measured and installed as the plant baseline. Resolution (era-2c full-plant retest): the family had TWO causes - v8's low-speed period-doubling vanished once measured armature+friction were installed (1.30 -> 2.50 Hz, bifurcation-edge machine sensitivity), while v7's stood untouched at 1.20 Hz (saturation-freeze-driven policy property) - both evidence lines paid off, one per case.

  Mechanism

  A behavior appearing only on hardware has candidate causes on both sides of the sim2real boundary; changing training to fix it tests only one side per expensive cycle. Two cheap parallel probes - one intervening on the reward mechanism, one making sim reproduce the real behavior - localize the cause to a side before any training money is spent, and sim-reproduction of a real pathology is itself the strongest form of plant validation.

  Applies when

  • a gait pathology appears on hardware but never in any simulator
  • deciding whether a sim2real gap is reward-side or plant-side
  • tempted to change the gait clock/reward to chase a hardware symptom

  “倍周期(真机 1.23~1.32 Hz ≈ 时钟一半,v6/v7 连续两代;sim 从不出现):两条证据线互为对照——(a)… 真机重跑看频率是否回 2.5 Hz(假说:v7 没有任何奖励把步态钉在时钟上,真机 plant 有 armature/摩擦、高频贵,自由滑落到复摆自然频率 ~1.1 Hz);(b)真机吊挂录 fit_actuator.py … 看能否在仿真里复现 1.25 Hz。谁先给出阳性结果谁定 v9 的方向。”

• FK-verify a borrowed reference's structure, then size its amplitude by the reference's job - it pins phase, the policy adds liftreference-structure-fk-amplitude-division

  Mechanism understoodwalkreward-shapingreward-shapingcurriculumplant-calibration

  When borrowing a reference trajectory: verify its structural claim against your own kinematics (an invariant like flat-foot), assign it the phase-pinning job, and size amplitude low enough that the policy contributes the lift - moving toward a proven foreign value in halves, not jumps.

  Symptom

  walk_v4 had big knee swing (40-46 deg) but only 18-24 mm foot lift - amplitude without hip/knee/ankle phase coordination; later, walk_v5's real-robot swing ballooned to 73.6 deg (sim 55.7) with violent footfalls - amplitude over-driven by the reference.

  Context

  Structure first: Humanoid-Gym's 1:2:1 hip:knee:ankle reference was verified on the local model before adoption - the ratio exactly satisfies the locally derived flat-foot constraint hip - knee + ankle = 0, FK-tested at multiple amplitudes with sole pitch 0.00 deg throughout. Amplitude second, and here the first reasoning failed honestly: FK said shorter legs need LARGER reference scale (0.30 for 30 mm lift), and the FK was correct - but the premise was wrong ("FK 没错, 但前提错了"): it assumed foot lift must come from the reference. HighTorque Pi, same scale, uses 0.08 with a 0.02 m foot-height target - proof that lift is added by the policy ON TOP of the reference, whose actual job is pinning the phase relationship. Scale 0.30 made the reference the entire gait: over-constrained and over-driven. The correction went to 0.15, deliberately not Pi's 0.08: "一次只走一半, 留退路" (walk half the distance, keep a retreat).

  Change

  target_joint_pos_scale 0.30 -> 0.15 as one of v6-minimal's three changes, treating both the footfall force and the lateral kicking (yaw momentum scales with leg swing amplitude).

  Outcome

  v6 improved landing force 1.72x -> 1.55x, suspended tilt 45.9 -> 23.0 deg, turn-gain asymmetry 70% -> 19%; the later v6-halved-shaping experiment (35 mm -> 4 mm collapse) confirmed the reference still carries the gait's existence on this machine - the division of labor is real but machine-specific.

  Mechanism

  A joint-space reference plays two separable roles: encoding structure (phase relations that keep the foot flat) and injecting amplitude (energy). Structure transfers across robots and is checkable by FK against an invariant; amplitude is a negotiation with the policy, and over-assigning it to the reference removes the policy's freedom to modulate lift with state.

  Applies when

  • importing a reference gait / imitation target from another codebase
  • reference amplitude reasoning based on leg length alone
  • real swing amplitude far exceeds sim's under a strong reference

  “FK 没错, 但前提错了。我默认抬脚必须由参考轨迹产生。HighTorque Pi 同尺度机器人 … 用 0.08, 而它 target_feet_height = 0.02 m —— 说明抬脚是策略在参考之上加出来的, 参考只负责钉住髋/膝/踝的相位配合。我们取 0.30 等于让参考本身就是整个步态, 过约束 + 过驱动”

• Set torque limits per joint from measured gait peaks - a uniform percentage is the wrong shape, and training must use the deployed numberstorque-limit-shape-by-measured-peaks

  Mechanism understoodwalkactuator-modelingactuator-modelinghardwareplant-calibration

  Measure per-joint torque peaks in the actual gait and set each limit as measured-peak x margin capped at rating; then propagate the same numbers into training and add an automated deploy-time consistency check - never derate by a uniform percentage, never let training assume torque deployment will not grant.

  Symptom

  A uniform 50% torque derating (18/8.5/7) had piled safety margin on the joints that never use it while cutting the busiest joint below half its measured demand.

  Context

  Per-joint gait peaks were measured (walk_v5 at cmd 0.3/0.6): RS06 (hip_pitch/knee) uses 5.5-5.9 N*m = 15-16% of its 36 N*m rating - cutting it to 12 is a free safety win; RS02's ankle_pitch runs at 16.2 N*m = 95% of its 17 N*m rating - "它是速度的硬件瓶颈", no room to cut; RS00 measured 36-44%, capped at 11. The resulting shape 12/17/11 replaced the uniform percentage. Sweeps across several limit sets (rated / 50% / 14-17-11 / 12-17-11) produced identical speed, lift, and landing force - within this range the limits do not shape the gait; what matters is consistency: "关键是训练和硬件必须是同一个数", because the exporter fills effort_limit from tau_limit, and a policy trained at rated 36/17/14 "会假设有三倍力矩可用" while deployed at 12/17/11 (exactly the v5 cross-generation inconsistency later suspected in its wild kicking).

  Change

  robot.yaml tau_limit set to the measured-shape 12/17/11, firmware written to match, and train/isaac_values.py regenerated so training sees the same limits; the deploy tool self-checks limits against robot.yaml on every run.

  Outcome

  Free safety margin captured where demand is low, the real bottleneck joint left at rating, and the train/deploy torque worlds unified with an automated consistency check.

  Mechanism

  Torque demand is grossly unequal across joints in a gait (15% vs 95% of rating here); a uniform percentage misallocates the safety budget by construction. And since the trainer treats effort_limit as a plant truth, any train/deploy mismatch is an invisible plant gap of exactly the mismatch ratio.

  Applies when

  • choosing safety torque limits for a legged platform
  • training-vs-deployment actuator limit audit
  • one joint runs near rating while others idle

  “曾用统一 50%(18/8.5/7)是错的形状: 把余量堆在用不到的 RS06 上, 却把 ankle_pitch 砍到需求的 52%。… RS02 在 0.6 m/s 已用到额定 95%, 它是速度的硬件瓶颈 … 实测多组限幅 … 完全一致 —— 限幅在这个范围对步态零影响, 关键是训练和硬件必须是同一个数。… 若训练仍按额定 36/17/14, 学出的策略会假设有三倍力矩可用。”

• Every power cycle starts with the same read-only pre-flight - read the buses, check the torque limits against 12/17/11, verify the IMU axes, check the ports after any new USB device - and any reassembly re-measures the joint zerospower-cycle-preflight

  Observed onceinfrareal-deployreal-acceptancehardwareprocess

  Start every powered session with a fixed, read-only pre-flight - bus responses, torque limits equal to the simulated ones, IMU axes, device identities - and re-measure joint zeros after any mechanical reassembly before running a policy.

  Symptom

  Hardware state drifts between sessions in ways no policy can see: a motor that stops answering after a power cycle, a torque limit that differs from the one simulated, an IMU axis flipped, two USB devices swapping identities, a joint zero moved by reassembly.

  Context

  The runbook's session order before any policy runs: read every motor on both CAN buses without enabling them (the first command after every power cycle); set_torque --check, all twelve motors must read 12/17/11 N*m, and any difference is written back; imu_reader --verify-axes, where the operator tilts the robot forward and to the right and every check must pass before continuing; check_ports after plugging in any new USB device (the IMU and a CAN adapter once collided on USB identity). After re-mounting motors: read the buses, then re-measure the calibration offsets (three repeats, written back) - "skipping it means running everything on the wrong zero". Hanging checklists repeat the torque-limit check (the deploy script also self-checks at start).

  Change

  A fixed, read-only pre-flight run in the same order every session.

  Outcome

  The runbook records one earlier hardware check in the same spirit: all 12 motors' implied kp fell within 18.4-22.0 for a commanded 20, inside the kp randomization range used in training.

  Mechanism

  A policy transfers only if the plant matches the one it was evaluated on; the pre-flight turns silent hardware drift into a failed check before the robot moves.

  Applies when

  • the first command after powering a robot on
  • after swapping adapters, cables or motors
  • a policy that worked last session suddenly behaves differently

  “python tools/set_torque.py --check # 12 颗应全对 12/17/11, 有 diff 就 --write … 插任何新 USB 设备后都先跑一次 check_ports.py(IMU 和 CANable 的 USB 身份撞过车) … python tools/calib_stance.py --repeat 3 --write # 重标 offset —— 8/9/10 重新装, 机械零位变了”

• A binary reward band on the swing knee had zero gradient everywhere below it, so the one-leg policy parked in an unloaded "fake touchdown" that Isaac's 5 N threshold scored as success and MuJoCo showed as real pressing - a capped constant-gradient ramp, retrained from scratch, passed 40/40binary-band-reward-fake-touchdown

  Mechanism understoodonelegreward-shapingreward-shapingsim2sim

  Shape approach-to-target rewards as capped ramps with gradient from the starting posture, never as bands or indicators; and compare contact-based terms across simulators, because a policy riding just under a force threshold looks perfect in one and wrong in the other.

  Symptom

  At iteration 1,000 of the first one-leg run the swing foot never lifted: the policy stood with the "raised" foot resting lightly on the ground. In Isaac the contact-match term paid 96% of full marks; the same policy in MuJoCo pressed that foot on the ground for 450 frames.

  Context

  The swing-leg goal was "shank folded fully back" (knee 1.5-1.95 rad), rewarded as a binary band: +0.8 inside [1.5, 1.95], zero elsewhere. From knee 0.05 to 1.5 rad the term was flat. Contact is judged at a 5 N force threshold, so a foot carrying less than 5 N counts as lifted. The walk line had hit the same disease with a binary indicator (v4) and fixed it with a capped ramp (knee_swing_amplitude).

  Change

  swing_knee_fold changed from the binary band to a ramp clamp(|q|/1.5, 0, 1) - a constant gradient capped near 86 deg - and the policy was retrained from scratch (V0r1). After the first real-robot try showed the fold still too low, its weight went 0.8 -> 2.0 (V0.1).

  Outcome

  V0r1 model_2300 passed the full acceptance 40/40 (swing knee 1.72 rad, about 98.5 deg) and was stamped as oneleg_v0.onnx; the cross-simulator disagreement is recorded as the thing that caught the cheat.

  Mechanism

  A reward that is flat until the target is reached gives no gradient to approach it, so the policy settles for the nearest state other terms reward - here, a foot that satisfies the contact threshold without lifting; a second simulator with different contact force resolution exposes such threshold-riding.

  Applies when

  • rewarding a posture target with an in-band / out-of-band indicator
  • a contact threshold decides whether a foot counts as lifted
  • trainer-side contact terms are near full marks while the video looks wrong

  “初版二值带 [1.5,1.95] 在膝 0.05→1.5 全程零梯度，策略停在"卸力虚点地"（Isaac 5N 阈下 contact_match 96% 满分 / MuJoCo 同策略 450 帧实压——跨仿真器互证抓作弊）；v4 二值指示同型病，按 knee_swing_amplitude 判例改常数梯度封顶 ramp，从零重训 … **oneleg_v0.onnx = V0r1 model_2300, 40/40 PASS**”

• After seven patch-generations, freeze the lineage as a regression baseline, fix the structural debts, and retrain from zerofreeze-lineage-fix-structure-restart

  Observed oncewalkprocessprocesscontract-freezecurriculum

  When successive rungs keep trading one symptom for another, ask whether the remaining problems are structural (contracts, latency, sampling, reward-table architecture); if so, freeze the lineage as regression baselines, pay the structural debts, and restart minimal - carrying forward laws and instruments, not weights and weights' patches.

  Symptom

  The v5-v11 walk lineage had accumulated interacting patches (reward terms, gates, clamps, per-joint scales) faster than it converged on the user's goal; v12's spec itself was superseded before training by an external review's verdict that the remaining problems were structural, not parametric.

  Context

  The 2026-08-05 status banner records the pivot: the walk profile was rolled back wholesale to v10b parameters, the v5-v11 lineage frozen "只作回归对照" (kept only as regression baselines), and four structural debts were named as prerequisites for a from-zero straight-walk baseline: the action-latency FIFO (fixed with its own test), the ONNX manifest contract, discrete command sampling, and a minimal reward table. The v12 spec - fully designed, partially implemented - was suspended: "本规格挂起,不再按此开训".

  Change

  Strategy switched from "one more patch generation" to freeze-fix-restart: lineage checkpoints retained as comparison anchors, infrastructure hardened first, then a clean retrain with a minimal reward table (this restart produced the s* generation that later became the real-robot SOTA line).

  Outcome

  A designed-and-ready training generation was deliberately not run - the review's structural findings outranked sunk design cost; the restart line inherited seven generations of laws (calibrations, gate batteries, falsified fixes) without inheriting their entangled reward table.

  Mechanism

  Patch lineages accumulate coupled terms whose interactions eventually cost more to reason about than a restart costs to train; the knowledge worth keeping is the laws and instruments (measured plant values, calibrated gates, falsified directions), not the entangled weights. A restart on hardened structure converts the lineage's lessons into a clean initial design instead of another delta.

  Applies when

  • repeated rungs shuffle symptoms without net progress
  • an external review flags infrastructure/contract debts
  • deciding between another patch generation and a clean retrain

  “同日外部评审定调换路线:冻结 v5~v11 血统(只作回归对照),修结构性问题(latency FIFO 已修 tests/test_action_latency.py、ONNX manifest 契约、离散命令采样、最小奖励表)后从零训直行基线。本规格挂起,不再按此开训。”

• A frame-history observation under zero DR memorizes the trainer's plant fingerprint - the estimator must see variation to learn estimationhistory-obs-needs-plant-variation

  Mechanism understoodomniobservation-designobservation-honestydomain-randomizationsim2sim

  If the observation carries history (stacked frames, RNN), keep at least minimal plant variation (gain/latency jitter) on from the first iteration - "nominal first, robust later" is structurally invalid for estimator-bearing contracts.

  Symptom

  omni_s1 (fresh 215-dim contract with a 5-frame history window, trained with DR fully off): training all green, yet the MuJoCo gate scored 0/20 on all eight doors - falls within 2 s, seven checkpoints, not one transferred.

  Context

  The history window exists precisely to let the actor implicitly estimate line velocity and actuator dynamics (the actor is denied base_lin_vel by observation honesty). Under a constant plant that implicit estimator has nothing to estimate - it learns the trainer's exact response fingerprint instead, and any other simulator's micro-differences are out-of-distribution: "5 帧窗按设计就是隐式估计器, plant 恒定时它学到 Isaac 精确响应的指纹". The planned "nominal-first-robust-later" staging was declared STRUCTURALLY incompatible with history observations: "估计器要见过变化才学估计, 否则学背诵" (an estimator must see variation to learn estimation, otherwise it learns recitation). Honest confound note kept: this is mixed with "zero DR does not transfer, period" - but both attributions prescribe the same fix, so no control was run.

  Change

  S1.1: minimum actuator jitter turned on from day one - kp/kd +/-10%, latency 0-1 frame (friction/COM/mass still nominal, no push - those stay for the S2 ladder).

  Outcome

  Transfer restored: survival 0/20 -> 20/20, speed 19/20, foot distance 20/20 (remaining failures moved to gait quality, a different disease); the staging doctrine was amended - history-carrying contracts never train under a frozen plant.

  Mechanism

  A recurrent/history channel fits whatever temporal structure minimizes loss; with a deterministic plant the cheapest structure is the plant's own impulse-response signature, yielding features that are simulator-specific rather than physics-general. Plant variation forces the channel to carry state-estimation features that transfer.

  Conflicts

  Attribution is explicitly confounded with the simpler "zero DR never transfers" reading ("与「零 DR 本身就不迁移」混杂 … 两种归因处方相同, 不做对照") - the source chose not to spend a control run separating them.

  Applies when

  • adding frame stacking or recurrence to an actor observation
  • a nominal-plant policy fails a cross-simulator gate within seconds
  • planning DR staging for a new contract

  “frame_hist × 零 DR = plant 指纹过拟合——5 帧窗按设计就是隐式估计器, plant 恒定时它学到 Isaac 精确响应的指纹, MuJoCo 的微小差异即 OOD, 2 s 内摔, 七个 checkpoint 无一迁移。「先标称后鲁棒」的分段与历史观测结构性冲突:估计器要见过变化才学估计,否则学背诵。”

• Decompose the offending quantity by channel first - then penalize the failure event, not the jointspenalize-the-slip-not-the-joint

  Mechanism understoodwalkreward-shapingreward-shapingattribution

  Before penalizing motion to fix a side effect, measure which channels actually carry the offending quantity; prefer penalties conditioned on the failure event that are exactly zero for healthy behavior - and do not medicate behaviors that measurement shows are not sick.

  Symptom

  Heading drift with support-foot yaw slip (v5: 212-284 deg accumulated over 15 s); the previous v6 draft had attacked it by penalizing lateral joints (a roll 4.0 / yaw 2.0 "home" group) - which collapsed training into the standing basin.

  Context

  Before choosing the penalty target, the yaw angular momentum was decomposed by joint group with MuJoCo subtree_angmom weighted by real walking joint velocities: pitch-class joints (hip_pitch + knee) carry 95.3%, hip_roll 3.3%, hip_yaw 1.4%. The failed "home" group had been taxing 2.7/step to manage a 4.7% channel. The replacement, feet_yaw_slip (-0.2, |support-foot yaw rate| while in contact), targets the failure event itself and - decisively - costs a non-slipping gait exactly zero, which "横向回家组做不到". The same rung's do-not-do table applied the complementary principle to foot spacing: measured 196-214 mm, stable, no crossing - "没病不吃药" (no disease, no medicine).

  Change

  Removed joint-usage penalties for the drift problem; added the event-conditional slip penalty (-0.2, realized tax 0.141/step = 12% of tracking) alongside the existing linear-slip term.

  Outcome

  Turn-gain left/right difference improved 70% -> 19% and heading 185 -> 60.3 deg by v6 without a standing-basin collapse; the 2.7/step lateral tax never returned.

  Mechanism

  Penalizing joints taxes every use of a channel including healthy use, and if the channel carries little of the offending quantity the tax buys nothing while pushing the optimum toward immobility. An event-conditional penalty (slip while in contact) prices only the failure, leaving the healthy gait's cost surface untouched - and the channel decomposition tells you in advance whether a joint-side fix can even work.

  Applies when

  • choosing a penalty target for drift/slip/impact problems
  • a proposed penalty taxes joints or motions rather than failure events
  • a previous joint-penalty attempt collapsed the gait

  “pitch 类 (hip_pitch + knee) 占偏航角动量 95.3% … hip_yaw 1.4% … 压 hip_yaw 是管 1.4% 的通道收 2.7/步 的税 —— 上一轮正是这样把策略推进了站立盆地。滑移项不惩罚走路: 不打滑的步态代价为零, 这是横向"回家"组做不到的。”

• Reward fixes come in causal chains - foot height, then landing impact, then foot spacingreward-chain-foot-height-landing-spacing

  Observed oncewalkreward-shapingreward-shaping

  Plan reward shaping as a chain, not a point fix: when you patch a degenerate gait behavior, pre-register which adjacent behavior the optimizer will exploit next and watch for it.

  Symptom

  Three problems appeared strictly in sequence: (1) swing feet lifted too low; (2) after fixing that, feet slammed down - "实际比视频里暴力得多" (far more violent in person than on video); (3) after fixing that, feet drifted too close together and collided.

  Context

  Each reward fix removed one degenerate optimum and exposed the next. The fix for foot spacing (COM lateral randomization +/-5 cm to force leg spread) itself caused base side-to-side sway, requiring a further foot-to-centerline distance penalty. Lucen had just solved its own foot-height problem (19mm -> 40mm swing height) and logged landing impact and foot spacing as the predicted next two problems.

  Change

  Chain of additions - (1) penalty when swing foot below 5 cm; (2) landing vertical-velocity penalty at touchdown; (3) COM lateral randomization +/-5 cm, then foot-centerline distance penalty to cancel the induced sway.

  Outcome

  Reference robot progressed through each stage; each individual fix worked and predictably surfaced the successor problem. For Lucen the chain served as a pre-registered roadmap of what breaks next.

  Mechanism

  Locomotion rewards are coupled through contact dynamics: raising swing height adds potential energy that must go somewhere at touchdown (impact); penalizing impact and forcing robustness to COM shifts changes lateral support strategy (spacing/sway). The optimizer always exploits the cheapest unpenalized channel, so fixing one channel routes the exploit to its neighbor.

  Applies when

  • adding a foot-height / clearance reward
  • feet slam or landing impact grows after a clearance fix
  • feet converge toward the centerline or self-collide
  • any single-reward fix to a coupled gait behavior

  “抬脚太低 → 加惩罚：摆动足低于 5 cm 就扣分 / 加完之后砸脚 → 抬起来了但落地极猛，"实际比视频里暴力得多" → 加落地速度惩罚 … / 两脚太近甚至互撞 → 先试质心横向随机化 ±5 cm … 有效但引发新问题——基座开始左右摇摆 → 再加足-中心线距离惩罚 … 这三条是串联的：每个修复都会暴露下一个问题。”

• The best checkpoint to SHIP is not the best checkpoint to CONTINUE FROM - maturity is capital against adaptation shockroot-maturity-vs-product-quality

  Mechanism understoodomnifork-selectionfork-selectioncurriculumgate-battery

  Decide shipping points and fork roots separately: gates rank products, but a root candidate must prove itself by surviving a continuation under the next rung's shift (dual-arm if in doubt) - and prefer the more-trained point as root when product metrics conflict with maturity.

  Symptom

  A band re-audit found s1e-300 beat the incumbent root s1e-500 on nearly every quality gate (stepping 19/20 vs 13/20 with historically-best 26.9 mm swing, speed gate 14/20 vs 2/20, heading 26 vs 54 deg/20 s) - suggesting the root had been mis-picked and the younger point should take over.

  Context

  The dual-arm control settled it the other way: continuing the S2 PD rung from s1e-500 adapted smoothly (3/3 smoke throughout), while the b300 control arm (same config, from s1e-300) fell into a survival valley under the PD shock (+100 iters: 1/3 -> 0/3), never climbed out within budget, and its 800-iter product scored 13/20 survival - eliminated. Verdict: "幼年点自身指标再好也扛不住新 DR 适应冲击, 成熟度是本钱,s1e-500 根被数据背书" - a young point's own metrics, however good, do not survive new-DR adaptation shock; maturity is capital. The audit still yielded value: the band scan (200-1000, per-100) mapped the lineage's arc (200 dragging -> 300 peak -> 400+ decay -> 900+ drift blowout), and 300 remains the better PRODUCT answer for shipping-as-is questions.

  Change

  Selection doctrine split into two questions with different answers: best-product point (quality gates at the point itself) vs best-root point (survives adaptation shocks; more training age = more capital), each decided by its own evidence - and root claims settled by a dual-arm continuation test, not by point metrics.

  Outcome

  s1e-500 kept the root role with data behind it; the S2e ladder built on it passed rung after rung, while the b300 line was closed at the cost of one control arm.

  Mechanism

  Early checkpoints sit near sharp optima with less accumulated robustness structure; their headline metrics reflect the narrow training distribution, not resilience to distribution shifts. A continuation rung is itself a distribution shift, so the root property being selected for is shock tolerance - observable only by actually continuing, never by static gates.

  Applies when

  • a younger checkpoint outscores the current root on quality gates
  • choosing the base for a robustification or command ladder
  • a continuation run stalls in an early survival valley

  “b300 对照臂 … PD 冲击下存活谷(+100 起 1/3→0/3),预算尽未爬出,800 档 20-seed 存活 13/20 出局——幼年点自身指标再好也扛不住新 DR 适应冲击,成熟度是本钱,s1e-500 根被数据背书”

• A torque-tail penalty was paid for by bracing the legs against each other - the second simulator's leg-contact count caught it, and the first explanation ("the trainer can't see self-collision") was retracted from the run's own configtorque-penalty-bought-by-leg-bracing

  Observed oncerecoverysim2sim-gatesim2simreward-shapingattribution

  When a penalty lowers a demand metric, look for what the policy traded to get there - keep self-contact frames and foot spacing as standing sim2sim readouts - and check any "the trainer cannot see X" explanation against the run's resolved config before it enters the record.

  Symptom

  After R3.1's torque_headroom term collapsed the demand tail, MuJoCo success fell 100 -> 98% and leg-leg contact frames at mu 1.0 rose 750 -> 2,190 (worst rollout 177 -> 450). The one failure (prone seed 2) had the legs crossed, one foot on the other leg, trapped at 0.067 m - visible on video.

  Context

  Across the ten prone seeds, foot spacing and leg-leg contact frames were monotonically anti-correlated, and the failure was the extreme of the series. Pulling the legs toward the midline shortens the hip_roll lever arm and lowers torque demand. At the time the spec explained it as Isaac training without self-collisions ("a free lunch in a simulator without self-collision").

  Change

  Leg-leg contact frames and foot spacing were tracked in every MuJoCo gate; R3.2's candidates were "train with self-collision on" or "a minimum leg spacing term" - not stacked.

  Outcome

  The next rung's joint-velocity penalty incidentally erased the dependency (2,190 -> 86 frames). On 08-10 the runs' logged env.yaml showed enabled_self_collisions true in both r3_1 and v2_2 (inherited from walk v10): the tangle was physically learned bracing, visible to both simulators, and the Isaac/MuJoCo contact-count gap was mesh and contact fidelity. The "self-collision debt" narrative was withdrawn for the whole line.

  Mechanism

  A penalty on demand rewards any configuration that lowers demand; legs pressed together act as a mutual support that fails when contact geometry shifts slightly.

  Conflicts

  §24 attributes the dependency to self-collisions being disabled in training; §36 retracts that from the runs' env.yaml ("§24's mechanism explanation was wrong") and keeps the older sections unedited as history.

  Applies when

  • a torque, impact or energy penalty improves its metric and cross-sim success drops
  • legs or links approach each other after a regularization change
  • an explanation relies on a simulator setting nobody checked in the run config

  “prone 十个 seed 逐条看,脚距与腿-腿接触帧数单调反相关, 而唯一失败的那条正是最极端的一条 … 机制上说得通:把腿收到身体中线附近能缩短 `hip_roll` 力臂、降低力矩需求”

• Deployment power derating damages non-forward axes far more than forward - sweep it in sim before deployingpower-scale-hurts-nonforward-axes

  Replicatedomnireal-deployreal-acceptanceactuator-modelingattribution

  Treat deployment power/torque scaling as a plant parameter: evaluate the policy in sim at the exact deployment scale, expect non-dominant axes to degrade first under derating, and either deploy at the training power or train with power randomization.

  Symptom

  Policies deployed at power-scale 0.8 (a safety derating of commanded torque) looked fine walking forward but were weak at backward and turning, inviting the wrong diagnosis "the skill was not trained well".

  Context

  Measured repeatedly: on s1e, going 1.0 -> 0.8 cost forward 18% but backward 58%; on C4-ff800, turn tracking was +25%/+40% at pw0.8 vs +75%/+58% at pw1.0, backward 51-52% vs 97-103%, while forward stayed 96-98% at both. Sim evaluation numbers in the plan were all pw1.0, but the robot was being run at 0.8.

  Change

  Pre-deploy protocol added: sweep the exported policy across power in sim (for PW in 0.8 0.9 1.0: eval_c_matrix --power $PW --seeds 20) and deploy at the first level where both turn directions reach >=50%. For C4 the recommendation was raise the robot to pw1.0 - the sweep showed it nearly free (saturation 47%->33%, left foot-clipping danger zone 25%->6%, cost only tilt 6.7->8.3 deg).

  Outcome

  Turning "weakness" resolved without any retraining; the sim sweep correctly predicted the real-robot signature at both power levels.

  Mechanism

  Forward walking is the reward-dominant, torque-cheapest skill with the most margin; backward/turn/sidewalk live closer to the torque envelope, so a uniform torque derating consumes their margin first. Training ran at power 1.0 (the trainer does no power scaling), so deploying at 0.8 is a systematic underactuation the policy never experienced.

  Applies when

  • deploying with any torque/power derating or safety scale
  • secondary skills (backward, turn, lateral) underperform on hardware while forward walking looks fine
  • choosing the deployment power level for a new policy

  “power 衰减对非前进轴的伤害远大于前进轴（s1e：前进 1.0→0.8 掉 18%，后退掉 58%）。转向是非前进轴，0.8 下很可能明显跟不动。”

• Teleop fed the sidewalk axis a command beyond its training band - feet clipped; give each axis its own speed settingteleop-command-band-per-axis

  Mechanism understoodomnireal-deployreal-acceptancehardwareattribution

  Give every command axis its own teleop scale, clamped to that axis's training band, and reproduce any hardware incident in sim with the exact deployed command values before touching training.

  Symptom

  Robot stepped on its own foot when sidewalking left under teleop - and only when going left.

  Context

  The teleop tool used one speed setting for all axes: --teleop-speed 0.20 applied to A/D sent cmd_vy = 0.20, above the training band's top (0.08-0.18) where foot-spacing margin is thinnest. Sim reproduction of the incident (product policy, pw0.8, 5 seeds x 20 s, true collision threshold = single foot width 104 mm): at vy 0.20 the minimum foot distance was 111-115 mm - 7-11 mm from self-collision - vs 147 mm at vy 0.10. Left was 4x more dangerous than right (25% vs 6% of time inside the 160 mm soft wall at vy 0.10), matching the left-only symptom; the margin did not degrade over time (pressing more just lengthened exposure).

  Change

  deploy_policy gained --teleop-side (default 0.10), separating the lateral speed from the forward speed so each axis's teleop command sits inside its own trained band.

  Outcome

  Command now inside the band with 43 mm margin at default; the incident became a quantified, reproduced, closed account rather than a mystery.

  Mechanism

  The policy's competence envelope is the training command distribution per axis; teleop mappings that share one scalar across axes silently command out-of-band inputs on the weakest axis. Asymmetric risk (left vs right) came from the policy's own chirality bias, so a symmetric command produced an asymmetric hazard.

  Applies when

  • wiring a joystick/teleop layer over a learned policy
  • a hardware incident occurs on one command direction only
  • training bands differ across command axes

  “A/D 一直与 W/S 共用速度档，所以按 A 下发的是 vy = 0.20 —— 既超训练带（0.08~0.18）上沿 … 0.20（遥控实际值）| 111~115 mm | 7~11 mm … 且左比右危险 4 倍 … 处置：deploy_policy 新增 --teleop-side（默认 0.10），侧移与前进档分开。”

• Raising a command bucket's share does not strengthen its per-state gradient - it only starves the other modesbucket-share-is-not-a-gradient-lever

  Mechanism understoodomnicurriculumcurriculumreward-shapingdomain-randomization

  When a skill is not learning, first prove its per-state signal is nonzero (ignore-floor and probe checks); only rebalance sampling shares to fix genuine sample starvation, and account the regression risk to the diluted modes before doing it.

  Symptom

  Sidewalk was not learning, and the reflex proposal was to give the side bucket a larger share of sampled commands.

  Context

  The C4-redo3 rung explicitly kept the 20/40/20/20 bucket (stand/forward/turn/side) with the reasoning written out: PPO computes advantages per state, so bucket proportion does not change the per-state gradient of side states; at 4096 envs x 20% x 24 steps the rollout already contained ~19.7k sidewalk states - sample count was not the bottleneck. And the cost side was already measured: cutting forward from 60% to 40% had made vx+0.30 die at +400 in an earlier run - more cuts would only collapse it sooner.

  Change

  Bucket proportions held constant across the entire C4 redo series; the actual bottlenecks (metric frame bug, reward variance penalty, exploration form) were pursued instead.

  Outcome

  Sidewalk was eventually fixed with zero bucket changes (feed-forward delivery, +100 iters); forward/turn skills never suffered starvation-induced regressions during the redo series.

  Mechanism

  Policy-gradient credit is assigned per visited state; oversampling a mode multiplies its states in the batch but not the informativeness of each, so if the per-state gradient is ~0 (behavior unreachable or reward indifferent), N times zero is still zero - while the displaced modes genuinely lose data and regress.

  Applies when

  • proposing to oversample a failing task/command mode
  • a majority mode regresses after share rebalancing
  • budgeting env count vs mode share for a multi-skill policy

  “比例不动：PPO 逐状态算优势，桶占比不改变单状态梯度；4096 env × 20% × 24 = 每 rollout 已有 1.97 万个侧走状态，样本数不是瓶颈；而 forward 60%→40% 已实测让 f30 在 +400 处死掉，再加码只会更早塌。”

• Exponential tracking kernels go flat exactly when the error is largest - pair them with an L2 term for the far fieldexp-kernel-needs-l2-far-field

  Mechanism understoodwalkreward-shapingreward-shaping

  Never let an exp/Gaussian kernel be the only tracking pressure on a quantity that can drift far from target: pair it with an unbounded (L2) term sized as the "don't diverge" floor, and check which frame the kernel reads.

  Symptom

  With only an exp-type yaw tracking term (exp(-err/std^2), std 0.25), a robot whose heading had drifted badly received almost no corrective gradient: at error 0.6 rad/s the term evaluates to exp(-0.36/0.0625) = 0.003 - near zero AND flat.

  Context

  The exp kernel is excellent for fine tracking near zero error but its gradient vanishes at large error - precisely when correction matters most. Fix: add track_ang_vel_z_err_l2 (-0.5), a plain quadratic on the same quantity: "exp 管精细跟踪、L2 管'别发散', 互补". Both terms deliberately read WORLD-frame wz (matching the exp term's source), because this torso sways enough that body-frame wz means are systematically off (measured -0.039 while actually turning +0.152). The same far-field-gradient argument reappears in the v8 risk list: frozen joints could not climb back because their huge error put them on the exp plateau ("远端梯度消失是冻结自锁的帮凶").

  Change

  Added the L2 companion term at -0.5 alongside the existing exp term (a term that had been in an earlier draft and was lost in a rewrite - itself worth noticing).

  Outcome

  Corrective pressure restored across the whole error range; the exp+L2 pairing became the house pattern for tracking terms.

  Mechanism

  d/de[exp(-e^2/s^2)] -> 0 as e grows: the kernel saturates and cannot distinguish bad from terrible. A quadratic's gradient grows with error, covering the far field; summing the two yields monotone corrective pressure with fine shaping near the target.

  Applies when

  • tracking rewards use exp/Gaussian kernels alone
  • a drifted or frozen state fails to recover during training
  • designing tracking terms for quantities with large transient errors

  “exp 在误差大时梯度趋零, 恰好在最需要纠正的时候失灵。… 误差 0.6 → exp(-0.36/0.0625) = 0.003, 接近零且平坦。… exp 管精细跟踪、L2 管"别发散", 互补。”

• The run policy never left the ground and fell in the second simulator from the frontal plane - its DR (gains and latency only) covered the actuator axis, not the frontal-plane contact and inertia disturbances the doubled stride amplified; "is DR on" is the wrong questionthin-dr-judged-by-channel-coverage

  Replicatedrundr-tuningdomain-randomizationsim2simattribution

  Judge a DR recipe by whether its randomized terms cover the channel where the skill can lose stability, not by whether DR is enabled; when a new skill lengthens single support or enlarges motion in one plane, add disturbances in the plane it destabilizes before training.

  Symptom

  run R1 (6,000 iterations, 78 min): no flight phase ever appeared, and every one of 13 checkpoints failed the eight-gate MuJoCo smoke. In Isaac: zero terminations in 6,000 iterations, 4.2 deg tilt. In MuJoCo at delay 2: 1/6 survived, falls within 1.9-6.2 s at 50.8-58.7 deg, the most saturated joints all roll joints.

  Context

  The run contract doubled sagittal travel (knee action scale 0.9, knee swing peak 1.14 rad) with a 0.60 s period and 0.40 duty - long single support - while roll/yaw scales were deliberately left at 0.5. DR copied the s1e recipe: kp/kd (0.9, 1.1) and latency on; mass, COM, joint friction and push all off; ground friction pinned at (1.0, 1.0). Flight was read two independent ways: Isaac's per-foot contact reward stayed 0.845-0.857, never above 0.87 - the arithmetic ceiling of a gait with zero flight - and 30 of 36 MuJoCo seeds had flight fraction exactly 0 (the nonzero six were all tumbling falls). Foot lift itself worked (46-59 mm against a 50 mm design point): the walk-era "not enough travel" failure did not recur.

  Change

  Verdict FAIL, with the pre-registered first knob (exploration noise 1.0 -> 1.2) explicitly rejected as aimed at a different axis. The lesson was generalized and applied at the next line's design review: the one-leg spec made push, body mass, base COM and friction DR mandatory for its permanent single support and banned the thin recipe.

  Outcome

  The run line did not continue past R1 in the sources. The one-leg V0 with the wider DR passed its friction-variant gate (mu 0.4 and 1.2) inside a 40/40 acceptance.

  Mechanism

  Randomizing gains and latency covers the actuator's axis; a skill whose failure lives in frontal-plane contact and inertia needs randomization on that channel (push, mass, COM, friction), or the trainer's exact plant becomes the only one the policy can stand on - the omni_s1 transfer trap a second time, this time with DR switched on.

  Applies when

  • a policy is flawless in the trainer and falls immediately in a second simulator
  • reusing a DR recipe from a skill with a different support pattern
  • failures concentrate on one axis (roll, yaw) the DR does not touch

  “**机理**: 矢状面行程翻倍 (膝摆动峰 1.14 rad) + T 0.60 + duty 0.40 的长单支撑, 把额状面扰动放大了一个量级; 而 roll/yaw 通道按 §3 **刻意没有放大** (仍 0.5), DR 又是 s1e 复刻的薄配方 (mass/COM/关节摩擦/push **四关全关**, 地面摩擦钉死 (1.0, 1.0))。 … 说明**薄 DR 的判据不能只看"有没有开 DR"**, 要看**开的那几项 是否覆盖失稳所在的通道** —— kp/kd 与延迟是执行器轴向的, 对额状面接触/惯性 扰动零覆盖。 … 0.87 正是「零腾空的走路步态」的天花板算术”

• mj_objectVelocity returns inertial-principal-axis frame - one API assumption poisoned eval and observations for a whole linebody-frame-velocity-api-audit

  Mechanism understoodomnisim2sim-gatemeasurementsim2simobservation-honestyattribution

  Verify every frame-sensitive API against a hand-computed truth (rotate raw qvel yourself, or command a known world velocity and check where it lands) before trusting any evaluation or observation built on it - especially when a model's inertial frame is rotated from its body frame.

  Symptom

  Sidewalk vy read ~0 under every condition; separately, whole-policy performance was mysteriously mediocre in sim2sim while training-side numbers looked fine. Four training rungs were declared FAIL partly on these readings.

  Context

  base_link's URDF inertial frame is rotated 90 deg about x relative to the body frame (iquat = [0.7071, 0.7071, 0, 0]). mj_objectVelocity(flg_local=1) rotates into ximat - the inertial principal-axis frame - not the body frame, and returns center-of-mass point velocity, not body-origin velocity. Consequences measured: the "vy" column was actually vertical velocity vz (walking at cmd 0.25: old reading +0.0093 vs true -0.0424); the angular velocity fed to the policy in sim2sim was [wx, wz, -wy] - a different quantity than Isaac and the real IMU provide. RMS check over 8 s of walking: y/z axes swapped between v6[:3] and the qvel truth.

  Change

  Fixed sim2sim and both probes to compute ang_b = qvel[3:6] and lin_b = xmat.T @ qvel[0:3] (identical quantity to Isaac's root_ang_vel_b / root_lin_vel_b), with a standalone reproduction script (frame_bug_repro_0809.py).

  Outcome

  Re-scoring the "failed" C4 lineage under correct coordinates reversed the verdicts: c4r4 checkpoints showed vy 80-126% tracking (old reading: +/-2%) and vx+0.30 at 91-95% where the old metric said 0/5 - the bad frame both mis-measured vy and, via corrupted policy observations, systematically depressed all measured performance. Final product passed 260/260 cells.

  Mechanism

  A simulator API's frame convention is part of the observation contract; when the model's inertial frame is rotated relative to the body frame, frame-agnostic use of a "local" velocity silently permutes axes. Feeding a policy an axis-permuted angular velocity is an observation corruption that degrades behavior everywhere, not just on the axis being studied.

  Applies when

  • building or auditing a cross-simulator evaluation harness
  • one measured axis reads near-zero under all conditions
  • sim2sim scores are inexplicably worse than training-side metrics
  • URDF/MJCF inertial frames are rotated relative to body frames

  “base_link 的 iquat = [0.7071, 0.7071, 0, 0] … mj_objectVelocity 用的是这个 … 喂给策略的 base_ang_vel 是 [wx, wz, −wy] —— MuJoCo 侧观测与 Isaac / 真机 IMU 不是同一个量；vy_mean 报的是竖直速度 vz —— 前进 cmd 0.25 时旧读数 +0.0093，真值 −0.0424。”

• Audit which joints your imitation term constrains - a task that needs deviation is fighting the referenceimitation-term-scope-audit

  Mechanism understoodomnireward-shapingreward-shapingcurriculum

  List which joints your imitation/deviation terms actually constrain and check the new skill's required motion against that list; for balance-coupled joints deliver references as feed-forward residuals, not absolute-position targets - and never assume "reference = 0" is neutral.

  Symptom

  Sidewalk would not learn despite a dedicated tracking reward; meanwhile the gait-shaping imitation term (joint_pos_ref) computed its error norm over ALL 12 joints while its reference covered only the 6 sagittal joints - roll/yaw reference was constantly 0.

  Context

  Two prior generations had shown the forward gait itself was taught by joint_pos_ref, not discovered by PPO (v6 halved the shaping and swing height collapsed 35 mm -> 4 mm). So the reference is load-bearing - but sidewalk requires hip_roll to deviate from nominal, and the all-joints norm punished exactly that deviation: "一边悬赏一边罚过程" (posting a bounty while punishing the process). A follow-up experiment (C4-redo3, free_roll=True releasing the 4 roll joints from the norm) raised the regularization headroom 6x -> 27x yet sidewalk stayed flat and released hip_roll wandered, killing other skills - net negative, withdrawn. A --roll-absolute probe showed the converse failure: pinning roll to a clock-driven absolute trajectory drove tilt 6.9 -> 13.7 deg. Conclusion recorded: absolute-position imitation cannot teach actions that must be superimposed on state feedback.

  Change

  The audit reframed the problem: neither punishing roll deviation nor freeing roll nor absolute roll tracking works; the reference for a balance-coupled joint must be delivered as feed-forward under the policy's residual control (see feedforward-for-phase-locked-skills).

  Outcome

  free_roll rung: joint_pos_ref term rose 0.887 -> 1.104 (release confirmed effective) but vy stayed flat; regularization hypothesis eliminated by experiment.

  Mechanism

  An imitation error norm defines a cage: joints inside it are pulled to the reference in absolute position, so any skill requiring systematic deviation is taxed per step; but joints carrying active balance cannot follow absolute references either, since their correct position depends on state. The scope and the delivery mechanism of the reference are therefore design decisions per joint, not defaults.

  Applies when

  • adding a skill that moves joints your reference sets to zero/nominal
  • an imitation or deviation penalty coexists with a new tracking reward
  • considering releasing joints from a shaping term mid-lineage

  “前进步态也不是 PPO 自己发现的，是 joint_pos_ref 教出来的（v6 砍半塑形 → 抬脚 35 mm 塌到 4 mm…）。而 ref_joint_offset 原本只写 6 个矢状面关节，roll/yaw 参考恒 0 —— 侧走既没被教，roll 一偏离 nominal 反被 joint_pos_ref 扣分。一边悬赏一边罚过程。”

• PPO's Gaussian noise cannot compose phase-locked oscillations - deliver them as feed-forward and let the policy learn the residualfeedforward-for-phase-locked-skills

  Mechanism understoodomnicurriculumcurriculumreward-shapingcontract-freeze

  If a skill needs a temporally coherent (phase-locked) action component, do not expect step-wise exploration to find it: inject a verified feed-forward and train the policy as a residual stabilizer, keeping the feed-forward inside the deployment contract.

  Symptom

  Four different reward arrangements (no reference / wrong-sign reference / correct-sign reference / cage released) all failed to elicit sidewalk, while open-loop probes proved the behavior existed and was safe on the same platform with the same policy as base.

  Context

  Producing lateral velocity requires a phase-locked hip_roll oscillation synchronized to the gait clock. PPO's exploration is per-step, zero-mean, uncorrelated Gaussian noise - it can never compose a sustained phase-locked component, so the behavior is unreachable by exploration regardless of how it is rewarded. The fix changed the delivery channel: target = default + scale*action + lat_ff(cmd_vy, phi). The policy's action becomes a residual on top of the feed-forward, retaining full balance authority (it can even cancel the feed-forward); the feed-forward supplies exactly the component exploration cannot. This mirrors why the sagittal joint_pos_ref worked (it also delivered phase structure), just via a different channel.

  Change

  Contract-level change, done cleanly: new profile omni_ff (= omni + lat_ff_gain -0.5), existing omni profile bit-identical; feed-forward applied after the action delay stage; missing cmd/phase raises instead of silently dropping; deployment must use the same phi as build_obs (recomputing gives a one-tick phase misalignment).

  Outcome

  From C2-700, +100 iterations sufficed: product omni_c4_ff800 scored vy +120%/+125% (from +4%/-1%), 260/260 cells at 20/20 survival, zero old-skill regression, left/right gap 5 pp - the entire C4 saga resolved by changing the delivery mechanism, not the reward.

  Mechanism

  Exploration noise spans only the subspace its correlation structure can express; skills requiring coherent oscillation lie outside the span of i.i.d. per-step noise. Feed-forward moves the required structure into the action pipeline where it needs zero probability mass to appear, reducing the learning problem to stabilizing around a demonstrated behavior - which PPO does well.

  Applies when

  • a periodic/oscillatory skill trains flat under every reward variant
  • open-loop injection of the behavior already works
  • considering GRU/curriculum/exploration tricks for a rhythmic skill

  “病因不在奖励，在探索形式：产生侧向速度需要相位锁定的 hip_roll 振荡，PPO 的逐步高斯噪声零均值无相关，合不出相位锁定分量。… target = default + scale·a + lat_ff(cmd_vy, φ)。策略动作因此是前馈之上的残差，保留全部平衡权限”

• Borrow reward values from other robots as ratios (to tracking weight, to leg length) - never as absolute numberstransfer-ratios-not-absolutes

  Mechanism understoodwalkreward-shapingreward-shapingprocess

  When importing any numeric from another robot's config or paper, identify its natural normalizer (tracking weight, leg length, sqrt(g*L), body mass) and transfer the dimensionless ratio; sanity-check against a same-scale robot when one exists.

  Symptom

  Published configs offered tempting absolute values (swing height target 0.06-0.08 m, weight -20) that would have been wrong for a robot with half the leg length and a different tracking weight.

  Context

  The cross-check normalized before transferring: G1's feet_swing_height weight -20 against tracking +1.0 is a 20x ratio, so with local tracking at 1.5 the equivalent is -30, not -20. G1's 0.06 m target on a ~0.70 m leg scales to ~28 mm on the local 0.325 m leg (Humanoid-Gym converts to ~23 mm), confirming the locally chosen 0.03 m and explicitly rejecting copying 0.05-0.08 absolutes. A same-class robot (Menlo's 16 kg) was used to sanity-check feet_air_time (+0.5 vs the local 2.0, flagged over-high). A nondimensional check of the same kind later validated the sidewalk speed target (v/sqrt(gL) = 0.081 vs hardware-verified 0.101 - 0.104 - inside the envelope, conservative).

  Change

  All borrowed values converted through ratios (weight/tracking-weight, height/leg-length, dimensionless speed) before entering the config.

  Outcome

  The scaled values worked (0.03 m target matched both the scaling law and measured 22-23 mm baseline); no cross-robot absolute was ever copied raw.

  Mechanism

  Reward economies are scale-relative (only ratios to the tracking term matter to the optimum) and kinematic quantities are morphology-relative (clearance scales with leg length, speed with sqrt(g*L)); absolutes encode the source robot's scale, ratios encode the design intent.

  Applies when

  • copying reward weights/targets from open-source configs or papers
  • setting clearance heights, speed targets, or impact thresholds
  • comparing your weights to published tables

  “G1 的 feet_swing_height 是 tracking 的 20 倍(−20 vs +1.0)。我们 tracking 是 1.5,按同比例应为 −30 … G1 目标 0.06 m / 腿长 ~0.70 m,换算到我们 0.325 m 腿长约 28 mm;Humanoid-Gym 换算约 23 mm。故 target 取 0.03 m 是对的 … 不必抄 0.05~0.08 的绝对值。”

• Audit rewards by realized contribution (weight x achieved value) - a weight of 2.0 was really paying 0.04realized-contribution-audit

  Mechanism understoodwalkreward-shapingreward-shapingattribution

  Evaluate a reward table by each term's realized per-step contribution under the current policy, never by its weight column; if a term's realized value is ~0, escalating its weight is a no-op - change the term's structure instead.

  Symptom

  Foot dragging persisted through repeated weight escalation: feet_air_time had been raised 0.25 -> 1.0 -> 2.0 across versions with no behavioral change, and the training-side comment even recorded the fact ("几乎没有 单支撑相, 是拖着脚蹭") without the fix changing form.

  Context

  Computing realized per-term contributions in the trained state exposed the economy: feet_air_time contributed weight 2.0 x achieved 0.019 = 0.038 per step, against tracking's +1.20 - lifting the leg earned 3% of what tracking earned, so dragging was the rational optimum no matter the weight escalation. The same table acquitted the energy penalties (total -0.30 negative vs +1.74 positive) that a naive read of weights (-5.0 orientation!) would have blamed.

  Change

  Fix redirected from "raise the weight again" to "add a term whose realized contribution changes the optimum": a clearance penalty sized so its realized magnitude (~0.018/foot when dragging) is comparable to feet_air_time's, enough to flip the optimum without drowning tracking.

  Outcome

  With the term economy corrected (plus posture/range fixes), swing height reached 34 mm and tracking 87% by v6; weight escalation of the old term was abandoned.

  Mechanism

  A reward weight is only a multiplier on whatever the policy currently achieves on that term; when the achieved value is near zero (behavior absent), escalating the weight multiplies near-zero. Optimizer behavior is governed by realized per-step magnitudes, so audits must be conducted in that currency.

  Applies when

  • a behavior persists despite repeated weight increases
  • auditing whether penalties are "too strong" or rewards "too weak"
  • sizing a new reward term against existing ones

  “把 feet_air_time 权重从 0.25 → 1.0 → 2.0 一路加,但没有加高度项。量级算下来:feet_air_time 权重 2.0 × 实得 0.019 = 0.038,而跟踪奖励是 1.2。抬腿的边际收益只有跟踪的 3%,拖地当然是最优解。… 正项 +1.74,负项 −0.30。能量惩罚不是瓶颈,抬腿没收益才是。”

• An edge-triggered landing penalty missed the tail and fired after the harm - penalize overspeed continuously inside the contact windowpenalize-tail-before-touchdown

  Mechanism understoodwalkreward-shapingreward-shaping

  Penalties aimed at impact/violation events must (a) price the excess over a threshold, not the mean, and (b) be active on the approach (state-gated window), not triggered by the event - check your control rate can even see the event you are penalizing.

  Symptom

  The v7 landing penalty (vz^2 on the contact-force rising edge, weight -10) did not bite: landing-velocity 95th percentile stayed at 2.61 m/s against a 0.3 target.

  Context

  Two structural faults were identified: (1) it penalized the MEAN over sparse events - many soft landings dilute the occasional violent slam, while the damage (GRF peaks, motor peak load) lives in the tail; (2) it fired AFTER touchdown - at 50 Hz evaluation the rising edge is aliased by physics decimation, so the read vz is often the already-decelerated post-impact value: underestimated, and with no shaping gradient before contact. Replacement: continuous penalty while the sole is inside a height gate (h < 0.03 m): relu(-vz - 0.30) - only the excess over an allowed approach speed is penalized (tail only), and gradient exists for several frames BEFORE touchdown. The sole-height computation again subtracts the 0.0585 m link offset ("WALK_DIAGNOSIS 坑#1, 别再踩"); the edge-triggered version was kept as a diagnostic only.

  Change

  feet_landing_vel reformulated: edge-event vz^2 -> in-window relu(-vz - v_ok) with v_ok 0.30 (conservative vs the sqrt(L)-scaled human value ~0.19, to be tightened after passing), h_gate 0.03, weight unchanged -10.

  Outcome

  The failure analysis of the first form was written before the second was trained; the v_ok escalation path (0.30 -> 0.45 if the robot becomes afraid to land) was pre-registered in the risk table.

  Mechanism

  Sparse-event mean penalties optimize the average case while the constraint is a quantile; and any penalty evaluated only at/after a discrete event gives the optimizer no gradient along the approach trajectory that determines the event. A state-gated continuous excess penalty fixes both: it prices only violations and shapes the approach.

  Applies when

  • impact/landing penalties fail to move tail percentiles
  • a penalty is triggered by contact edges at a coarse control rate
  • designing constraint-style penalties for rare violent events

  “罚的是均值路径:上升沿是稀疏事件 … 大量软着陆稀释偶发猛砸;而伤害在尾部 … 罚在触地后:50 Hz 评一次,上升沿被物理 decimation 混叠,读到的 vz 常是撞完已减速的值——既低估,又没有触地前的塑形梯度。”

• The first real-robot get-up was "very violent, kicking on the floor, dangerous" - a sim-perfect policy with no reason to be slow, unbounded absolute targets, no domain randomization and a rate limiter that filtered nothing; the task was restated as "safe, slow, transferable"first-real-get-up-violent-stage-one-policy

  Observed oncerecoveryreal-deployreal-acceptanceactuator-modelingattribution

  Do not put a get-up policy on hardware until its action is bounded (hard bound or state-anchored targets), smoothed, randomized and tested at the real pipeline's latency, and say explicitly that the task is "safe, slow and transferable" - a simulation-perfect policy optimizes only "gets up".

  Symptom

  On 2026-08-09 the user ran a V0-lineage recovery policy on the real robot and stopped it: very violent, kicking on the floor, dangerous. The planned next rung (a heavier torque_headroom) was never started.

  Context

  The spec had pre-registered that R0/R1 products stay in simulation and that the real-robot precondition was the R3 smoothing rungs plus a bridge-slew check plus a hanging protocol; the robustness (DR) rungs had not run. In simulation the policy passed 100% with a get-up of about a second. Which ONNX, which gain profile and whether a torque/joint log existed were left "to be recorded later" and never were.

  Change

  The V0 ladder was stopped at its best product (R3.1, sim only) and a re-rooting proposal was put to the user. The spec's four-layer account: style (the reward pays for standing early and nothing pays for slowness - HumanUP's "Stage I" get-up, "fast but unsafe ... infeasible for real-world deployment"); impact (full-range absolute targets with no hard bound, raw |a| up to 4.77, action saturation 100%, a single-step change of 0.306 saturating hip_pitch); transfer (zero DR, friction pinned at 1.0, the learned leg bracing); link (the bridge's RL slew equals vel_limit, 0.2-0.66 rad per step, while the real pipeline has 1-2 steps of time-varying latency and acceptance ran at delay 0).

  Outcome

  The line was re-rooted twice (training-side rate limit, then the beta-anchored action space) and gained a hang protocol before the next real attempt; on 08-11 a beta-anchored policy produced the line's first real get-up.

  Mechanism

  A task reward that pays for standing early selects the fastest feasible get-up; with absolute full-range targets every large target jump is a torque impulse bounded only by the clip; zero DR and braced-leg solutions do not transfer; and a limiter set at the velocity limit does nothing at 50 Hz.

  Conflicts

  The four layers are the spec's reconstruction from simulation probes and the literature; the real run's policy file, gain profile and log were never recorded, so no layer was confirmed against hardware data.

  Applies when

  • a first hardware trial of a high-effort skill is being scheduled
  • sim success is high but the policy saturates actions or torques
  • pre-registered hardware preconditions are not all met

  “用户真机反馈:**非常猛、地上乱踢、危险**,叫停(R3.3 torque_headroom 加档已选型 weight −0.5→−1.5,未启动)。真机细节(哪个 onnx、什么档、有无 τ/q log)**待补记** … 任务从"能起来"变成 **"安全、慢、可迁移"** … **链路层**:桥层 slew RL 档 = vel_limit(10/20/33 rad/s ≈ 每拍 0.2~0.66 rad), 对 recovery 形同虚设;真机 1~2 拍时变延迟,验收默认 delay 0。”

• Write each config's expected hardware signature before the session - and if reality disagrees, change the books, not the conclusionpreregistered-real-expectations

  Replicatedomnireal-acceptancereal-acceptanceprocesssim2sim

  Before hardware runs, write per-config expected signatures and the disagreement rule (hardware outranks sim; discrepancies get recorded, not reconciled); validate the harness by checking it reproduces at least one known real behavior.

  Symptom

  Hardware impressions are easily narrated after the fact; without written expectations, any real-robot outcome can be made to "match" the sim story.

  Context

  The S2 acceptance sheet carried a section titled "sim 侧预注册预期 (事后核对, 不许事后改)" - per-configuration behavioral signatures written before the session: s1e@0.8 the disturbance king (push 159/160, zero chirality, all-mu 20/20) at the cost of speed gates 0/20 and zero-command wander ~0.98 m with -29.5 deg/20 s rotation; fric-3000 "walks accurately but is easier to push over"; fric-2400 neither. Credibility check included: the sim harness had reproduced the already-recorded real behavior (pace in place + right drift + net rotation -30 deg/20 s), which "提高本单全部预期的可信度". The anomaly clause fixed the epistemics in advance: if results systematically disagree with sim, "不改结论改账" - don't massage the conclusion, write the discrepancy into the books, and per the earlier zero-warning lesson, hardware wins.

  Change

  Every hardware session ships with a pre-registered expectation table (signature per config), a baseline-match credibility check, and a written precedence rule for disagreement.

  Outcome

  The A/B session became falsifiable: agreement confirms the proxy, disagreement is booked as a proxy-bias finding rather than argued away.

  Mechanism

  Pre-registration converts qualitative hardware sessions into tests of the sim-to-real mapping itself; a reproduced known behavior calibrates trust in the remaining predictions; and fixing "who wins on disagreement" beforehand prevents authority from drifting to whichever source flatters the plan.

  Applies when

  • planning any hardware acceptance or A/B session
  • the sim harness's credibility in this regime is unestablished
  • post-session write-ups tempt narrative fitting

  “⚠️ sim 复现了真机已记录的「原地踏步 + 右漂 + 净旋 −30°/20s」—— harness 与真机行为对得上, 提高本单全部预期的可信度。… 结果与 sim 系统性不符 → 不改结论改账: 写进 README 该节, 按 「Isaac 指标三次零预警」的教训, 以真机为准。”

• Continuing a converged policy on a change that carried no new gradient drifted its transfer from 100/98% to 80/28% over 3,000 iterations while every Isaac gate stayed perfect - scan every checkpoint on the second simulator's friction axisconverged-continuation-is-poison

  Observed oncerecoverytraining-runfork-selectionsim2simcurriculum

  Before continuing a converged policy, check that the change creates a live gradient; if it does not, cap the budget at a few hundred iterations, and in every continuation scan each checkpoint on the second simulator's transfer axis (for example low friction) - trainer-side gates can stay perfect while transfer decays.

  Symptom

  V2.7-A (swap the flat_feet term for a compensated version, continue from v2_6c) finished with the line's best Isaac score (100%) and a MuJoCo transfer collapse: mu 1.0 98 -> 80%, mu 0.4 98 -> 28%; the stance it was meant to widen had not moved.

  Context

  The new term's calibration run showed a near-zero tax from the start: the policy already satisfied it, so the reward landscape offered nothing new. A checkpoint scan on MuJoCo mu {1.0, 0.4} located the damage: +100 iterations 100/98% (better than the baseline), then 86/54, 60/38, 80/28 - monotonic decay with training length, while entropy and action noise rose (7.77 -> 8.18, 0.588 -> 0.612): drift, not sharpening.

  Change

  Rule written in: with no new gradient, a continuation budget is short (at most a few hundred iterations) and the MuJoCo transfer axis enters every checkpoint scan. The next rung (V2.7b, a live stance-width gradient) was budgeted at 1,000 iterations with mu {1.0, 0.4} scans every 100 and a stop-on-signal rule.

  Outcome

  V2.7b kept transfer at the same depth (mu 1.0 98% / mu 0.4 92% at +1,000, where A had already rotted to 86/54) and at +3,000 (100/96%): a live gradient preserved transfer. V2.8 then broke that pattern (mu 0.4 2%): the gradient must also be compatible with the policy's existing form.

  Mechanism

  On a converged reward landscape PPO keeps updating without a signal to follow, and the random walk is pulled toward whatever the training plant rewards idiosyncratically - invisible in the trainer's own gates.

  Conflicts

  The drift mechanism is the spec's reading of one decay series plus one contrasting run; V2.8 is recorded as an exception to "live gradient keeps transfer".

  Applies when

  • fine-tuning a converged policy with a small reward change
  • a continuation run's trainer-side metrics improve while real or cross-sim results worsen
  • choosing which checkpoint of a continuation to ship

  “**checkpoint 扫定死因**(μ1.0/μ0.4):**29500(+100 iter)= 100/98%** (优于基线!)→ 30400 = 86/54 → 31400 = 60/38 → 32398 = 80/28 —— **迁移随续训长度单调衰减**。 … **教训入库:收敛均衡上的长续训是毒药 —— 无新梯度时 续训预算须短(≲数百 iter),且 MuJoCo 迁移轴必须进 checkpoint 扫描。**”

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